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New European Cybersecurity Regulations are coming! Increased Accountability for Corporate Leadership

Recent legal developments in Europe in the Cyber security field are set to impact businesses throughout Europe, particularly those involved in software development. With stricter regulations on the horizon, The NIS2 Directive is the EU-wide legislation on cybersecurity (The EU cybersecurity rules introduced in 2016 were updated by the NIS2 Directive).

Recent legal developments in Europe in the Cyber security field are set to impact businesses throughout Europe, particularly those involved in software development. With stricter regulations on the horizon, The NIS2 Directive is the EU-wide legislation on cybersecurity (The EU cybersecurity rules introduced in 2016 were updated by the NIS2 Directive). It provides legal measures to boost the overall level of cybersecurity in the EU. Companies must reassess and enhance their cybersecurity measures to comply with the impending changes.

Increased Accountability for Corporate Leadership

One key aspect of the new European policy is the heightened accountability placed on corporate leadership. Under the new regulations, company executives will be **explicitly** responsible for their organisation’s cybersecurity policies. Failure to fulfil these duties could result in personal liability, including potential temporary suspension from their positions.

Historically, many executives have delegated cybersecurity matters to IT departments, with their involvement limited to budget approvals. However, the upcoming regulations demand a more proactive role from them. They are now required to approve cybersecurity measures, oversee their implementation, and ensure the security of their direct suppliers.

Implications for Software Development Operations

Now that companies are digitally interconnected, a weak backdoor at a small supplier can affect the entire chain. The new regulations, therefore, compel large companies to take more responsibility for their suppliers. They must assist them, for example, by sharing their knowledge.

The rules will be enacted in the Netherlands in 2025 and in other EU countries. These regulatory changes necessitate organisations to complete their comprehensive review of procedures and enhance the security of software development operations. Developers must integrate cybersecurity considerations into every stage of the software development lifecycle, from design to deployment. This includes implementing robust authentication mechanisms, encryption protocols, and secure coding practices to mitigate cyber risks effectively.

Furthermore, software development companies must extend their cybersecurity efforts to encompass their supply chain. Collaborating with suppliers to enhance their security posture is essential in safeguarding the ecosystem against potential threats. For instance, make sure all your hosting providers and cloud providers have the necessary certifications in the cybersecurity field.

While the new regulations present challenges, they also create opportunities for innovation and growth within the software development industry. Companies proactively investing in cybersecurity measures can gain a competitive edge by demonstrating their commitment to data protection and risk mitigation. Additionally, the demand for cybersecurity professionals will likely surge, creating opportunities for skilled individuals in the job market.

The impending European cybersecurity regulations NIS2 underscore the critical importance of robust cybersecurity practices in software development operations. By proactively enhancing security measures and aligning with regulatory requirements, companies can mitigate risks, protect sensitive data, and ensure their corporate leadership is safe from personal liability because of security issues.

Introducing Agile Analytics by ZEN Software, your go-to solution for enhancing code security. Start your journey towards robust cybersecurity practices with Agile Analytics Leaks, a feature designed to fortify your code's defences. This tool seamlessly identifies leaked keys within your codebase, proactively mitigating the risk of unauthorized access and potential data breaches. By integrating Agile Analytics Leaks into your development workflow, you strengthen your software against evolving cyber threats. Embrace this opportunity to safeguard your digital assets effectively while experiencing the efficiency and reliability of the Analytics App.

Sign up now for a **60-day free trial** and discover the transformative capabilities of Agile Analytics firsthand.

 

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Traditional Analytics or Agile Analytics: which one fits you best?

 In this article, we explore the key differences between Agile Analytics and Traditional Analytics to help organisations make informed decisions about their analytical strategies.

All organisations do analytics to extract insights from their data. Some organisations even make most of their decisions based on this data. Are there any distinct ways in which we can do analytics? We’ve found two prominent approaches: Agile Analytics and Traditional Analytics. Each has its own set of principles, practices, and advantages. In this article, we explore the key differences between Agile Analytics and Traditional Analytics to help organisations make informed decisions about their analytical strategies.

As the name suggests, this form of analytics has been around since Big Gates was modelling on his desk back when Microsoft was still a cool startup.

**Sequential Process:**

Traditional Analytics typically follows a sequential, waterfall-like process. It involves distinct phases such as data collection, cleansing, transformation, analysis, and reporting. Each phase is completed before moving on to the next, often resulting in lengthy project timelines.

**Rigorous Planning:**

In the traditional approach, extensive planning is done upfront to define requirements and scope. Changes to the project scope are often challenging to accommodate once the project is underway.

**Fixed Scope and Deliverables:**

Projects are planned with fixed scope and deliverables, making it challenging to adapt to evolving business needs or to incorporate new data sources during the project.

**Comprehensive Documentation:**

 Documentation is a key aspect of traditional analytics. Detailed documentation is created at each stage to provide a comprehensive understanding of the entire process.

**Long Development Cycles:**

The emphasis on thorough planning and documentation often leads to longer development cycles, and insights are delivered at the end of the project.

Since the early 2010s everything needed to become ‘agile’, because as we learnt everything is agile is better? well, it depends but for analytics, approaching things in a more ‘agile’ fashion actually makes sense

**Iterative and Incremental:**

 Agile Analytics, inspired by the principles of Agile software development, adopts an iterative and incremental approach. It divides the project into smaller, manageable iterations or sprints, each delivering a potentially shippable product increment.

**Adaptive Planning:**

 Agile Analytics embraces adaptive planning, allowing teams to respond to changing requirements and priorities. Planning is continuous and flexible, enabling adjustments based on ongoing feedback.

**Embraces Change:**

 One of the core principles of Agile is its adaptability to change. Agile Analytics acknowledges that requirements evolve throughout the project, and it accommodates changes to ensure that the end product aligns with business needs.

**Collaboration and Communication:**

 Agile places a strong emphasis on collaboration and communication within cross-functional teams. Regular meetings, such as daily stand-ups, facilitate constant communication and coordination among team members.

**Early and Continuous Delivery:**

 Agile Analytics aims for early and continuous delivery of insights. This allows stakeholders to gain value incrementally throughout the project, rather than waiting for a comprehensive solution at the project's end.

Agile Software Development and Agile Analytics go hand in hand, sharing principles of flexibility and collaboration. Aligning analytics with an Agile mindset is crucial for the following reasons:

1. **Rapid Adaptation:** Agile development involves frequent requirement changes. Agile Analytics is flexible and adapts quickly to evolving project needs.
2. **Frequent Releases:** Agile development operates on short cycles. Agile Analytics ensures rapid insights to support the quick-release cadence of Agile teams.
3. **Collaborative Decision-Making:** Agile promotes team collaboration. Agile Analytics involves stakeholders, ensuring insights align with business goals.
4. **User-Centric Focus:** Agile emphasizes delivering value to users. Agile Analytics provides rapid feedback on user behaviour, enhancing user-centric solutions.

In summary, aligning analytics with an Agile mindset promotes collaboration, flexibility, and continuous improvement, ensuring timely and relevant insights for Agile development teams. This integrated approach supports data-driven decision-making and contributes to overall project success.

Experience ZEN Software’s Agile Analytics

The next step is to employ an Analytics platform based on the same tenets for your Software Development. With Agile Analytics, you measure critical metrics like feature/non-feature balance, deployment frequency, and SRE Error budgets and monitor DORA metrics and Error Budgets.

Measuring Error Budgets in Agile Analytics

Gain unparalleled visibility into your sprint performance. Don't miss this chance to break free from traditional constraints and propel your organization to new heights.

We can help you out with a DevOps Assessment, Continuous Delivery conversion, a Lean Software Development Transformation or an SRE implementation. Start your Agile Analytics journey today!

Try Agile Analytics

We’re confident we can supercharge your software operation.

supercharge, your, software, operation

What DORA Metrics Can Reveal About Your Team Performance

Ever wondered how fast your code goes from "I got an idea" to "Boom, it's live"? That's where Lead Time for Changes comes in – it's like your code's speedometer. Wanna know if your code changes are speedy like The Flash or just taking a stroll? In coding, finding the perfect rhythm is the key!

Ever wondered how fast your code goes from "I got an idea" to **"Boom, it's live"**? That's where *Lead Time for Changes* comes in – it's like your code's speedometer. Wanna know if your code changes are speedy like The Flash or just taking a stroll? In coding, finding the perfect rhythm is the key!

Lead Time for Changes is one of the DORA Metrics. Lead Time for Changes measures the time it takes for a code change to progress from the initial commit to being deployed and running in a production environment. This metric essentially quantifies the speed and efficiency of your development pipeline, offering valuable insights into the effectiveness of your team's workflows.


Key Components of **Lead Time for Changes** are:
- **Commit-to-Deploy**: This is the duration from the moment a developer commits a code change to the version control system to the point where it is successfully deployed in a production environment.
- **Review and Approval Time**: Lead Time for Changes includes the time spent on code review and obtaining necessary approvals before a change is merged into the main codebase.
- **Build and Test Time**: It accounts for the time taken to build and test the code change to ensure it meets quality standards and doesn't introduce regressions.
- **Deployment Time**: This is the time it takes to deploy the code change into the production environment, making it available to end-users.

So, what does Lead Time for Changes reveal about your team's performance?

**Short Lead Times:** Teams with short lead times demonstrate high efficiency in their development processes. For example, if a code change moves seamlessly from development to deployment in a matter of hours, it indicates an optimised pipeline.

**Potential Bottlenecks:** On the contrary, prolonged lead times may signal bottlenecks in various stages of the development lifecycle. For instance, a code review or testing delay might extend the lead time, highlighting areas that need attention and improvement.

**Short Lead Times:** A Short Lead Time for Changes reflects the team's agility and responsiveness to changing requirements. Suppose a team can implement and deploy user-requested features within a short timeframe. In that case, it suggests adaptability and a capacity to meet market demands swiftly.

**Long Lead Times:** Extended lead times may hinder a team's ability to respond promptly. For instance, if a competitor introduces a new feature, a team with long lead times may struggle to match the pace, potentially affecting its competitiveness in the market.

**Code Review Bottlenecks:** If lead times are primarily extended due to lengthy code review processes, the team might need to assess and optimize its review mechanisms. This could involve implementing tools for automated code review or establishing clearer guidelines for more efficient reviews.

**Testing and Deployment Challenges:** If testing or deployment times significantly contribute to the lead time, addressing these areas could involve investing in automated testing procedures or refining deployment scripts for faster and more reliable releases.

**Setting Reduction Targets:** By regularly monitoring lead times, teams can set targets for lead time reduction. For example, if the average lead time for changes is currently two days, the team might aim to bring it down to one day over the next few sprints.

**Iterative Process Refinement:** Teams can leverage lead time insights to refine their processes iteratively. If a retrospective analysis reveals that lead times increased after a certain process change, the team can revert or adjust the change and observe the impact.

The Lead Time for Changes metric serves as a versatile tool for teams to assess and enhance their development practices. Teams can pinpoint areas for improvement using DORA Metrics, foster a culture of collaboration and continuous improvement, and ultimately deliver higher-quality software more efficiently.

For teams keen on harnessing the power of DORA metrics, our amazing Platform, Agile Analytics, is ready to assist! Unlocking a wealth of insights, this app provides an **easy-to-set-up and easy-to-operate tool for measuring productivity and optimising development processes**. 

Curious to experience the transformative impact? **Take advantage of our free 60-day trial or book a demo with our talented team of Agile Coaches**. Witness firsthand how Agile Analytics can propel your team towards enhanced efficiency and success.

We’re confident we can supercharge your software operation. Our unique products and services will delight you.

Let’s talk!

What are Service Level Objectives (SLOs) and why should I measure them?

Have you ever wondered why some software applications perform better than others? The answer is simple: they have well-defined Service Level Objectives (SLOs).

SLOs are a critical component of Site Reliability Engineering (SRE) that defines the target level of service quality that a system should provide. I will explain what SLOs are, why they are important and provide examples of common SLOs.

SLOs, or Service Level Objectives, are **specific, measurable goals** that help software development teams ensure that their systems are functioning correctly and efficiently. The main purpose of SLOs is to set a threshold of acceptable service quality for a system, which is usually expressed as a percentage of uptime or response time. By measuring system performance against SLOs, teams can determine whether their systems are meeting their target service levels or need improvement.

 
Service Level Objectives (SLOs) are measurable goals that define the desired level of performance for a service. Different types of SLOs can be defined based on various aspects of service performance. Here are some common types:

**Availability SLOs**:
- *Definition:* These SLOs measure the percentage of time a service is available and operational.
-  *Example:* Achieving 99.9% availability means the service is allowed to be down for a maximum of 8.76 hours per year.    

**Latency SLOs**:
- *Definition:* Latency SLOs focus on the time it takes for a service to respond to a request or perform a specific action.
- *Example:* A latency SLO might specify that 95% of requests should be processed in under 100 milliseconds.    

**Throughput SLOs**:
- *Definition:* Throughput SLOs measure the number of operations a service can handle within a specified time frame.
- *Example:* Ensuring a web service can handle 1000 requests per second without degrading performance.    

**Error Rate SLOs**:
- *Definition:* These SLOs focus on the percentage of requests that result in errors or failures.
- *Example:* Keeping the error rate below 0.5% indicates a high level of reliability.    

Two of the most common SLOs are availability and latency. Let’s take a closer look at them and configure them in Agile Analytics.

Agile Analytics allows you to easily set up and manage Service Level Objectives for your services and alert when an SLO is out of target or when the corresponding Error Budget is depleted.

An Availability SLO defines the minimum acceptable level of uptime for a service over a given period. For example, an e-commerce website may have an availability SLO of 99.9%, meaning that the website should be up and running at least 99.9% of the time each month. Availability SLOs are crucial for ensuring that customers can access the service when they need it and can help teams prioritize efforts to improve uptime.

Set up tracking an Availability SLO in Agile Analytics is easy

tracking availability SLO in Agile Analytics

Common filters to measure availability (good-bad ratio):



A Latency SLO defines the maximum acceptable response time for a service. For example, a banking application may have a latency SLO of 500ms, meaning that the application should respond to user requests within 500ms or less. Latency SLOs are essential for ensuring that services are responsive and fast, which can help improve user experience and satisfaction.

Setting up a Latency SLO in Agile Analytics is super easy

Here is a common Latency SLO (Distribution cut) setup  in Agile Analytics:

**Threshold bucket:** 19
**Good Below Threshold:** True

“Measuring things brings knowledge” – Dutch proverb.

In the end, it's crystal clear that SLOs are a crucial part of keeping your software services up and running like a champ. With the top dogs being availability and latency, you can quickly measure how much time your service spends being a couch potato and how quickly it jumps into action when someone needs it. With Agile Analytics, you can track SLOs faster than Usain Bolt running the 100m dash, get instant insights into what's going on, and transform your services into a lean, mean performance machine. So, what are you waiting for? **Try out [the 60-day free trial of Agile Analytics](https://www.agileanalytics.cloud/start-free-trial) and get your software services in tip-top shape today!**

Start your [60-day free trial](https://www.agileanalytics.cloud/start-free-trial) or book a free demo call

Discover the power of agile metrics with Agile Analytics

Using machine learning algorithms for measuring performance

As the saying goes, "You can't manage what you can't measure." This is especially true in software development, where the success of a project hinges on the performance of the development team. But fear not! Machine learning is here to help.

As the saying goes, "You can't manage what you can't measure." This is especially true in software development, where the success of a project hinges on the performance of the development team. But fear not! Machine learning is here to help. With its powerful algorithms and ability to crunch through vast amounts of data, machine learning can provide a much more accurate picture of your team's performance. So, grab a cup of coffee and let's dive into the four ways machine learning can help measure your software development team's performance.

Predictive analytics is a branch of data analytics that uses machine learning and statistical algorithms to analyze past data and predict future outcomes. It involves the use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data patterns.

Predictive analytics can also be applied in software development teams to predict project outcomes, identify potential risks, and optimize team performance. Here are some examples of how predictive analytics can be used specifically in software development teams:

1. **Predicting project completion time:** By analyzing past project data, machine learning algorithms can predict the time required to complete future projects. This can help software development teams better plan their work and allocate resources more efficiently.
2. **Identifying potential risks:** By analyzing historical data and identifying patterns, machine learning algorithms can predict potential risks in a project. This can help development teams take proactive measures to mitigate those risks and avoid project delays or failures.
3. **Improving software quality:** Machine learning algorithms can analyze historical data on code quality, test coverage, and defect rates to predict the quality of future code changes. This can help development teams identify potential quality issues before they occur and improve the overall quality of their software.
4. **Predicting team performance:** By analyzing historical team data, machine learning algorithms can predict team performance, identify areas for improvement, and recommend coaching or training to team members. This can help development teams optimize their performance and achieve better results.

using DORA Metrics for performance predictions

Predictive analytics machine learning can help software development teams make more informed decisions, optimize their performance, and improve the quality of their software. As data becomes increasingly important in software development, the use of predictive analytics is likely to become even more widespread.

Time tracking involves using machine learning algorithms to track the time spent on each task or feature. This information can be used to identify areas where the team may be spending too much time, allowing organizations to allocate resources more efficiently.

1. **Time estimation:** Machine learning algorithms can analyze historical data from similar projects to estimate the time required for specific tasks. This can help development teams plan more accurate project schedules and allocate resources more efficiently.
2. **Time allocation:** Machine learning algorithms can analyze time-tracking data to identify where team members are spending the most time. This information can help development teams identify areas where more resources may be needed and allocate resources more effectively.
3. **Productivity monitoring:** Machine learning algorithms can analyze time-tracking data to monitor individual team member productivity. This information can help development teams identify where team members may need additional coaching or training to improve their performance.
4. **Task prioritization:** Machine learning algorithms can analyze time-tracking data to identify which tasks are taking the most time to complete. This information can help development teams prioritize their work and make adjustments to ensure that the most important tasks are completed first.
5. **Resource planning:** Machine learning algorithms can analyze time-tracking data to identify which team members are working most efficiently and allocate resources accordingly. This can help development teams optimize their resource allocation and improve overall team performance.

 machine learning enhanced time-tracking in Agile Analytics

Time-tracking machine learning algorithms can provide valuable insights into team performance and help development teams make data-driven decisions to improve their productivity and meet project deadlines. 

Resource allocation machine learning algorithms can provide valuable insights into how development teams can allocate resources more effectively to improve project outcomes. As software development projects become increasingly complex, resource allocation is likely to become even more critical for ensuring project success, and the use of resource allocation machine learning algorithms is likely to become increasingly common.

Sentiment analysis is a powerful application of machine learning that allows software development teams to analyze customer feedback, identify patterns and insights, and improve the quality of their software.

Here are some examples of how sentiment analysis can be used specifically in software development teams:

1. **Analyzing customer feedback:** By analyzing customer feedback through social media, email, and other channels, machine learning algorithms can identify common issues and complaints. This can help development teams identify areas for improvement and prioritize their work accordingly.
2. **Identifying user experience issues:** Machine learning algorithms can analyze user feedback to identify patterns in how users are interacting with the software. This can help development teams identify areas where user experience could be improved and make adjustments accordingly.
3. **Monitoring software reviews:** Machine learning algorithms can monitor online software reviews to identify common complaints and issues. This can help development teams prioritize their work and make improvements to their software based on customer feedback.
4. **Analyzing support tickets:** Machine learning algorithms can analyze support tickets to identify common issues and the frequency of those issues. This can help development teams prioritize their work and make improvements to their software to reduce the number of support tickets over time.
5. **Identifying sentiment in code reviews:** Machine learning algorithms can analyze code reviews to identify sentiment and the overall tone of comments. This can help development teams identify areas where team members may need additional coaching or training and improve the overall quality of their code.

Machine learning-based sentiment analysis can assist software development teams in gaining a better understanding of their customers, recognizing the areas where improvements are required, and making well-informed decisions backed by data to enhance the overall quality of their software.

In conclusion, with machine learning, you can say goodbye to subjective performance evaluations and hello to accurate data-driven insights. It's like having a team of highly skilled analysts working tirelessly in the background, except they don't take coffee breaks or nap time. By using machine learning algorithms, you can **optimize your team's performance, make informed decisions, and deliver high-quality software products**. So what are you waiting for? Embrace the power of machine learning and take your software development team's performance to new heights!

Discover the power of agile metrics and machine learning with Agile Analytics

From Riches to Rags: How Leaked Private Keys Led to the Disappearance of Code Spaces

In the world of software development, security breaches are an ever-looming threat. In June 2014, one company fell victim to a vicious cyberattack that ultimately led to its demise. This cautionary tale is a stark reminder of the importance of safeguarding sensitive information and highlights the need for Agile Analytics to mitigate such risks. 

In the world of software development, security breaches are an ever-looming threat. In June 2014, one company fell victim to a vicious cyberattack that ultimately led to its demise. This cautionary tale is a stark reminder of the importance of safeguarding sensitive information and highlights the need for Agile Analytics to mitigate such risks. Let's dive into the chilling account of Code Spaces and the catastrophic consequences they faced.

It all began when Code Spaces, a renowned code-hosting and collaboration platform, was under siege. A Distributed Denial of Service (DDoS) attack battered their defences. The perpetrators saw an opportunity to exploit a vulnerability and launched a full-scale assault on Code Spaces' infrastructure. The attackers had one motive: chaos. But little did they know how devastating their actions would be.

In an attempt to regain control and ward off the attack, Code Spaces' cloud team faced a harsh reality—their AWS management console had been compromised. The attackers infiltrated the heart of their system, gaining unauthorized access. The consequences were dire; the attackers started deleting critical data. A malevolent force that would soon bring Code Spaces to its knees.

As the attackers tightened their grip on Code Spaces, they unleashed a torrent of destruction. Valuable customer data and countless customer projects vanished into thin air. The devastation was absolute. The attackers seemed hell-bent on erasing any trace of Code Spaces' existence, leaving behind a bleak landscape of scorched earth.

In the face of such a catastrophic breach, Code Spaces' resilient team fought back. They sought to regain control of the data, salvaging what they could from backups. But the extent of the damage proved insurmountable as all the backups were corrupted or wiped by the attackers. Their failure to implement adequate backups and disaster recovery mechanisms left them helpless.

With their reputation tarnished and customer trust shattered, Code Spaces confronted a difficult decision. Their once-vibrant platform, a thriving collaboration hub, could no longer provide the reliability and security their customers deserved. In a heart-wrenching announcement, Code Spaces bid farewell to their users, acknowledging the impossibility of rebuilding what was lost.

Organisations must adopt proactive measures to protect their software and sensitive information in the face of lurking threats and potential devastation. [Agile Analytics](https://www.agileanalytics.cloud) is a formidable SaaS solution to mitigate the risks of leaked private keys, real-time DORA metrics and easy management of Error Budgets. By employing cutting-edge Cloud SaaS technology, you can scan your software, identify vulnerabilities, and take swift action to safeguard your organization from potential sabotage. Don't wait for disaster to strike—embrace the power of Agile Analytics today.

Code Spaces serves as a chilling reminder of the dangers related to security. Years of hard work and innovation were obliterated in a single stroke of malevolence. But from the ashes of this cautionary tale rises an opportunity for change. By embracing scanning and Agile Analytics, organisations can fortify their defence and ensure the security of their infrastructure. Don't let your organisation become the next victim—take control and protect what matters most.

Contact us to get a demo of Agile Analytics

No more Manual Hassles! Automate Your Non-Functional Measurement for Maximum Efficiency!

Software quality goes beyond functionality. Non-functional aspects like **performance**, **reliability**, and **security** are crucial. Our Agile Analytics SaaS platform offers a comprehensive solution to manage and measure these dimensions effectively. By leveraging DORA metrics and SRE Error Budgets, teams can optimise deployment, reliability, and security, while also monitoring software stock and detecting leaked private keys.

Software quality goes beyond functionality. Non-functional aspects like **performance**, **reliability**, and **security** are crucial. Our Agile Analytics SaaS platform offers a comprehensive solution to manage and measure these dimensions effectively. By leveraging DORA metrics and SRE Error Budgets, teams can optimise deployment, reliability, and security, while also monitoring software stock and detecting leaked private keys.

**1. [DORA Metrics](https://www.agileanalytics.cloud/manage-non-functional-quality-management-metrics-performance-reliability-security#dorametrics)**: Measure Deployment Frequency and MTTR Agile Analytics SaaS integrates  [DORA Metrics](https://www.agileanalytics.cloud/manage-non-functional-quality-management-metrics-performance-reliability-security#dorametrics), providing insights into deployment frequency and Mean Time between failure (MTTR). These metrics optimise software agility, adaptability, and recovery time, enabling data-driven decisions for continuous improvement.

**2. [SRE Error Budgets](https://www.agileanalytics.cloud/manage-non-functional-quality-management-metrics-performance-reliability-security#errorbudgets)**: Enhancing Reliability The platform introduces [SRE Error Budgets](https://www.agileanalytics.cloud/manage-non-functional-quality-management-metrics-performance-reliability-security#errorbudgets), defining acceptable error rates and quantifying software reliability. Monitoring error budgets allows proactive adjustments in planning and resource allocation to maintain reliability and enhance end-user satisfaction.

**3. Quantify [Software "Stock"](https://www.agileanalytics.cloud/manage-non-functional-quality-management-metrics-performance-reliability-security#softwarestock) and Detect [Leaked Private Keys](https://www.agileanalytics.cloud/manage-non-functional-quality-management-metrics-performance-reliability-security#leakedprivatekeys)**. Agile Analytics SaaS introduces innovative metrics to measure [software "stock"](https://www.agileanalytics.cloud/manage-non-functional-quality-management-metrics-performance-reliability-security#softwarestock) and detect[ leaked private keys](https://www.agileanalytics.cloud/manage-non-functional-quality-management-metrics-performance-reliability-security#leakedprivatekeys). Quantifying [software "stock"](https://www.agileanalytics.cloud/manage-non-functional-quality-management-metrics-performance-reliability-security#softwarestock) improves workflow efficiency, while detecting [leaked private keys](https://www.agileanalytics.cloud/manage-non-functional-quality-management-metrics-performance-reliability-security#leakedprivatekeys) safeguards sensitive data and prevents security breaches.

To excel in the digital landscape, software quality must prioritise non-functional aspects. The Agile Analytics SaaS platform **empowers** teams to optimise deployment, reliability, security, and workflow efficiency. By leveraging **[DORA Metrics](https://www.agileanalytics.cloud/manage-non-functional-quality-management-metrics-performance-reliability-security#dorametrics)** and SRE **[Error Budgets](https://www.agileanalytics.cloud/manage-non-functional-quality-management-metrics-performance-reliability-security#errorbudgets)**, organisations can make **data-driven decisions** for **continuous improvement**. Additionally, monitoring [software "stock"](https://www.agileanalytics.cloud/manage-non-functional-quality-management-metrics-performance-reliability-security#softwarestock) and detecting [leaked private keys](https://www.agileanalytics.cloud/manage-non-functional-quality-management-metrics-performance-reliability-security#leakedprivatekeys) ensures **enhanced productivity** and **security**. With Agile Analytics SaaS, automate your non-functional quality management and maximise your team’s ability to deliver!

I’m ready to supercharge non-functional quality management

Embracing Autonomy using Agile Analytics: You build it, you run it

In 2006, Werner Vogels, Amazon's CTO, introduced the now-famous mantra, "You build it, you run it," which has since become a guiding principle for many organisations. This philosophy encourages empowering teams to take ownership of the products they develop, fostering a sense of autonomy and accountability. 

In 2006, Werner Vogels, Amazon's CTO, introduced the now-famous mantra, "You build it, you run it," which has since become a guiding principle for many organisations. This philosophy encourages empowering teams to take ownership of the products they develop, fostering a sense of autonomy and accountability. By embracing this mindset, coupled with Agile Analytics' Error Budget approach, organisations can unlock the potential for more stable systems, engaged teams, and, ultimately, lower the cost of doing business.

Autonomy and the "You build it, you run it" approach

The essence of the "You build it, you run it" approach is to enable teams to have end-to-end ownership of the products or services they create. Traditionally, development teams were responsible for building software, while separate operations teams handled the task of maintaining and running it. This siloed approach often led to miscommunication, slowed down processes, and hindered overall efficiency.

By granting teams the autonomy to build and operate their products, organisations foster a culture of ownership, collaboration, and continuous improvement. When teams have direct responsibility for their creations, they are naturally incentivised to deliver high-quality work, prioritise stability, and respond swiftly to issues. This autonomy promotes faster iteration cycles, empowers teams to make decisions in real time, and enables them to develop a deep understanding of the systems they build.

Introducing Agile Analytics' Error Budget

To balance the need for new feature development with maintenance work, organisations can implement Agile Analytics' Error Budget framework. The concept of an Error Budget revolves around defining an acceptable level of system errors or service disruptions within a specified timeframe. This budget acts as a guiding metric that allows teams to determine how much time they can allocate towards new feature development without compromising system stability.

By establishing an Error Budget, organizations acknowledge that perfection is not the goal but rather achieving a balance between innovation and maintenance. It sets realistic expectations for teams and provides them with the flexibility to decide how to allocate their time and effort. With the autonomy to manage their Error Budget, teams can make informed decisions, adapt to changing circumstances, and take corrective action when needed.

Benefits of increased autonomy:

1. **Improved system stability**: When teams have end-to-end ownership, they become intimately familiar with the systems they build and can proactively address potential issues. This leads to greater system stability, reduced downtime, and improved customer satisfaction.
2. **Engaged and empowered teams**: Autonomy fosters a sense of purpose and ownership among team members. When they have control over their work and are accountable for its success, they become more engaged, motivated, and empowered to innovate and collaborate.
3. **Faster decision-making and iteration cycles**: Autonomy allows teams to make real-time decisions, eliminating the need for time-consuming approvals and dependencies. This agility enables faster iteration cycles, quicker response to customer needs, and the ability to adapt to market demands swiftly.
4. **Lower cost of doing business**: By empowering teams and streamlining decision-making processes, organisations can reduce overhead costs associated with unnecessary bureaucracy. Additionally, the increased focus on system stability helps minimise expensive downtime and subsequent customer dissatisfaction.

To initiate the optimization of your software operation, simply reach out to Agile Analytics. By adopting the "You build it, you run it" approach and leveraging our Error Budget framework, organizations can tap into the full potential of autonomous teams. With ownership over their products, empowered teams can enhance system stability, make informed decisions, and strike a beneficial balance between feature development and maintenance tasks. This autonomy not only fosters a culture of innovation and collaboration but also leads to substantial long-term cost savings.

Get more features delivered sustainably: as easy as connecting to Jira

Software developers often find new feature development to be the most enjoyable aspect of their work. It allows them to exercise their creativity, solve interesting problems, and deliver tangible results. Additionally, the team’s product owner’s primary focus is usually on adding new features to the product, as they believe it is what customers want and what will generate revenue.

Software developers often find new feature development to be the most enjoyable aspect of their work. It allows them to exercise their creativity, solve interesting problems, and deliver tangible results. Additionally, the team’s product owner’s primary focus is usually on adding new features to the product, as they believe it is what customers want and what will generate revenue. In this context, developers often prioritize feature development over other aspects, such as bug fixing or code optimization, because "we are being paid for features, not for bugs." Luckily there is now a solution available that is as easy as connecting to Jira: Agile Analytics Sprint Insights!

However, this singular focus on new feature development can have detrimental effects in the long run. Software that is constantly adding new features without proper attention to quality and stability can become slow, unstable, and insecure. Neglecting bug fixing and technical debt accumulation can lead to a deteriorating codebase and increased complexity, making it difficult to maintain and enhance the software over time. Moreover, security vulnerabilities may arise due to rushed development, lack of proper testing, or inadequate consideration of potential risks.

To address these issues, it is crucial to maintain a balance between feature development and non-feature work. Understanding the importance of allocating time and resources for bug fixing, code optimization, and security enhancements is essential for building a sustainable and robust software product. 

Leverage the power of AI and Large Language Models

Agile Analytics leverages the power of GPT and Large Language Models to differentiate between feature work and non-feature work by analyzing the content and structure of work items or tickets. By processing the textual information within these items, the model can determine the category of work that best corresponds to each item.

The underlying language model, such as GPT, has been trained on a vast amount of text data and has learned to understand the context and meaning of words and phrases. When applied to Agile Analytics, the model can interpret the descriptions, titles, and other relevant information provided within the work items. By analyzing this text, the model can identify patterns, keywords, or specific language that indicates whether the work item represents feature development or non-feature work, such as bug fixing or code refactoring.

Agile Analytics Sprint Insights further enhances this capability by allowing teams to train the model and fine-tune its performance.

Empowering your teams is as easy as [starting your free trial](https://www.agileanalytics.cloud/start-free-trial) and browsing to ‘settings’, ‘Sprint insights’ and pressing the **‘Connect to Jira’** button. Teams can gain visibility into the balance between feature and non-feature work today! 

To empower teams with insights into their software operation, Agile Analytics offers a solution that measures DORA Metrics, Sprint Insights, Error Budgets, Software Stock and detects Leaked Private Keys. 

Our amazing platform provides valuable analytics that can be used during Sprint Review sessions or as a bi-weekly sprint report, enabling the team to assess the stability and sustainability of their software. **Allow us to supercharge your software operation!**

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Stop Guessing, Start Measuring in your Quality Gate Meetings

Quality Gates serve as checkpoints or milestones within the software development lifecycle to ensure that specific quality criteria are met before progressing to the next phase. Too often, we see that while the process is implemented, the data that is ‘brought’ to the table is not empirical. We see a lot of HiPPO (Highest Paid Person’s Opinion) behaviour going on in these meetings. We believe we have a solution to that challenge! 

Quality Gates serve as checkpoints or milestones within the software development lifecycle to ensure that specific quality criteria are met before progressing to the next phase. Too often, we see that while the process is implemented, the data that is ‘brought’ to the table is **not empirical**. We see a lot of HiPPO (Highest Paid Person’s Opinion) behaviour going on in these meetings. **We believe we have a solution to that challenge! Stop Guessing; start Measuring!**

Using Agile Analytics' [Sprint Insights](https://www.agileanalytics.cloud/manage-non-functional-quality-management-metrics-performance-reliability-security#sprintinsights), powered by Large Language Models and GPT, teams can gain valuable insights into the **distinction between feature development and maintenance/non-feature work**. This capability enhances team autonomy by providing objective measures of the time allocated to each type of work. By leveraging advanced natural language processing, the tool can analyze and categorize tasks, allowing teams to understand how their time is being allocated and make data-driven decisions. This knowledge empowers teams to prioritize effectively, optimize resource allocation, and ensure a balanced focus on both feature development and maintenance, ultimately improving productivity and delivery outcomes.

Measuring the number of open changes in source control, often referred to as **['software stock'](https://www.agileanalytics.cloud/manage-non-functional-quality-management-metrics-performance-reliability-security#softwarestock)**, is crucial in understanding the amount of change accumulated and serves as a valuable quality metric. 

Measuring the number of open changes in source control, or the software stock, provides valuable insights into the amount of pending change and serves as a quality metric. By monitoring the stock and taking appropriate action, such as adjusting schedules or allocating resources, the development team can **minimize the risk** of encountering unstable conditions during the software's release to production.

Looking for Private Keys in your internal source control system is crucial to ensure that unauthorized access to protected cloud resources is prevented within your organization. Mistakenly committing a private key to the central repository can easily occur, and even if the key is removed from the latest commit, it may still exist in older revisions. By leveraging Agile Analytics' [Leaks Finder](https://www.agileanalytics.cloud/manage-non-functional-quality-management-metrics-performance-reliability-security#leakedprivatekeys), you can detect the presence of these keys in previous versions and take appropriate actions to secure your cloud resources. This becomes an essential security compliance metric, ensuring that sensitive information is not inadvertently exposed and **safeguarding your organization's data and systems**.

Setting up the process of [Error Budgets](https://www.agileanalytics.cloud/manage-non-functional-quality-management-metrics-performance-reliability-security#errorbudgets) empowers your teams by **granting them increased autonomy** in decision-making regarding feature development and quality aspects. [Error Budgets](https://www.agileanalytics.cloud/manage-non-functional-quality-management-metrics-performance-reliability-security#errorbudgets) provide a defined threshold of acceptable errors or issues in production. By having this framework in place, teams can determine whether they can focus on full-throttle feature development or if attention needs to be given to addressing quality concerns. This approach allows teams to strike a balance between innovation and maintaining high-quality standards, giving them the flexibility to make informed choices and prioritize their efforts effectively.

The industry standard DORA (DevOps Research and Assessment) metrics offer a valuable framework for measuring productivity and identifying high-performing teams while considering the quality aspects of software delivery. [DORA metrics](https://www.agileanalytics.cloud/manage-non-functional-quality-management-metrics-performance-reliability-security#dorametrics) encompass four key metrics: Deployment Frequency, Lead Time for Changes, Mean Time to Recover, and Change Failure Rate.

[DORA metrics](https://www.agileanalytics.cloud/manage-non-functional-quality-management-metrics-performance-reliability-security#dorametrics) enable the measurement of team productivity through quantitative indicators like Deployment Frequency and Lead Time for Changes. These metrics allow organizations to track and compare **productivity levels, identify bottlenecks, and drive improvement efforts**. Additionally, [DORA metrics](https://www.agileanalytics.cloud/manage-non-functional-quality-management-metrics-performance-reliability-security#dorametrics) incorporate quality aspects through Mean Time to Recover and Change Failure Rate, ensuring that productivity gains are achieved without compromising software quality. By including these quality metrics, organizations can identify high-performing teams that consistently deliver value while upholding high-quality standards.

At ZEN Software, we are ready to assist you with Agile Analytics, a comprehensive product that provides detailed reports for Quality Gate meetings and the Quality gate process. By leveraging real production data, we supercharge your software operation, enabling effective quality management in software development. Contact us to learn how we can enhance the quality of your software and drive your projects towards success.

How to Measure Software Development Productivity

As the software development industry grows, it becomes increasingly important to measure the effectiveness of your team and the software they produce. This is where Agile Analytics comes in, providing valuable insights into software development productivity.
Measuring productivity can be a challenge, but it is crucial to ensure that your team is working effectively and delivering high-quality software.

As the software development industry grows, it becomes increasingly important to measure the effectiveness of your team and the software they produce. This is where Agile Analytics comes in, providing valuable insights into software development productivity.

Measuring productivity can be a challenge, but it is crucial to ensure that your team is working effectively and delivering high-quality software. DevOps metrics such as lead time for changes and error budgets are key indicators of software development productivity. Lead time for changes measures how long it takes to get code from idea to production, while error budgets track the number of errors in your software and how much risk they pose.

Measure Productivity with Sprint Insights

In Agile methodology, sprints are time-boxed periods during which the team works on specific tasks. Sprint Insights refer to the data and metrics gathered during a sprint that help the team analyse progress and identify areas for improvement. By measuring Sprint Insights like Velocity, Burn-down Chart, Cycle Time, and Lead Time, analytics teams can track their productivity and optimise their workflow for better results.

Agile Analytics' Sprint Insights is a tool that employs deep learning technology to measure productivity by analysing millions of tickets from public sources. This advanced technology allows for the automatic determination of whether a ticket represents maintenance or improvement work and records the amount of time spent on both.

Machine learning is also becoming increasingly important in software development. We rely on Machine Learning, Language Models like chatGPT and OpenAI’s GPT itself for providing amazing features like Sprint Insights. By analysing data and identifying patterns, machine learning can help to improve software development productivity and feature development.

The benefits of using Sprint Insights to measure productivity are significant. By accurately tracking how much time is spent on maintenance versus improvement work, organisations can identify areas where their resources are being underutilised. This information can then be used to reassign staff to areas where their skills are most needed or to implement process improvements that will increase effectiveness.

Improve Collaboration with DevOps Metrics

DORA metrics are another valuable tool for measuring the effectiveness of your software development process. The DORA metrics (DevOps Research and Assessment) measure the performance of software teams in four key areas: deployment frequency, lead time for changes, time to restore service, and change failure rate. By measuring these metrics, you can gain insight into areas where your team can improve.

Agile methodology offers significant benefits to analytics teams looking to measure their effectiveness and optimize their workflow. By utilizing Sprint Insights to measure productivity, track software development progress, and improve collaboration with DevOps Metrics, analytics teams can unlock their full potential and deliver valuable insights to businesses.

In summary, measuring the effectiveness of your software development team is crucial to ensure that your software is of high quality and delivered efficiently. Utilizing Agile Analytics, DevOps metrics, DORA metrics, collaboration, and machine learning can help to measure productivity and improve the software development process.

Set up a Blameless Post-Mortem process in 7 steps

A post-mortem is a retrospective analysis of an incident or outage that occurred in a software system. A post-mortem aims to identify the root cause of the issue, evaluate the response to the incident, and develop a plan for preventing similar incidents from occurring in the future through improvements of the software system or processes.

A post-mortem is a retrospective analysis of an **incident or outage that occurred in a software system**. A post-mortem aims to **identify the root cause of the issue**, evaluate the response to the incident, and develop a plan for preventing similar incidents from occurring in the future through **improvements of the software system or processes**.

Post-mortems should be held as soon as possible after the incident has been resolved while the details of the event are still fresh in the minds of the team members involved. The analysis should involve a diverse set of stakeholders, including developers, operations personnel, and business leaders.

Post-mortems are critical to improving the reliability of software systems for several reasons:
1. **Post-Mortems help to identify the root cause of an issue**: By analysing the incident, the team can identify the root cause of the problem, which may be due to a bug in the code, a misconfiguration of the system, or a failure of a third-party service. Once the root cause has been identified, the team can develop a plan to address it.
2. **Post-Mortems enable continuous improvement**: By analysing incidents and developing plans to prevent similar incidents, the team can continuously improve the reliability of the system. This helps to ensure that the system is more resilient to future failures and that downtime is minimised.
3. **Post-Mortems promote collaboration**: Post-mortems require input from multiple stakeholders, including developers, operations personnel, and business leaders. This encourages collaboration and communication across teams, leading to a better understanding of the system and its dependencies.
4. **Post-Mortems foster a culture of accountability**: By conducting post-mortems, the team takes ownership of the issue and commits to improving the system. This fosters a culture of accountability and encourages team members to take responsibility for the reliability of the system.

To implement the SRE concept of a Blameless Post-Mortem, you can follow the following steps:
1. **Establish a culture of blamelessness**: It is essential to create a blame-free environment where team members can openly discuss problems without fear of being punished. Encourage team members to take ownership of their mistakes and share their experiences so that everyone can learn from them.
2. **Define the scope and objective of the post-mortem**: Decide what you want to achieve from the post-mortem. Define the scope of the investigation, such as the timeline of the incident, the systems and processes involved, and the impact of the incident on the business.
3. **Collect data and evidence**: Collect all the relevant data and evidence, such as logs, metrics, and incident reports. Use these to create a timeline of the incident and identify the root cause.
4. **Analyse the data and identify the root cause**: Conduct a detailed analysis to identify the incident's root cause. This should involve identifying the contributing factors, such as misconfigured systems, inadequate monitoring, or human error.
5. **Develop a remediation plan**: Based on the analysis, develop a remediation plan that addresses the incident's root cause. This plan should be focused on preventing similar incidents from happening in the future.
6. **Communicate the findings and remediation plan**: Share the findings of the investigation and the remediation plan with all relevant stakeholders. This should include both technical and non-technical team members.
7. **Follow up on the remediation plan**: Monitor the implementation of the remediation plan and track progress over time. This will help you identify any new issues and ensure that the plan is effective.

Before starting with the post-mortems, it is crucial to ensure that the following conditions are met:

1. **Leadership buy-in**: Top-level management should be supportive of the post-mortem process and encourage teams to participate.
2. **Team buy-in**: Team members should understand the benefits of the post-mortem process and be willing to participate.
3. **Resources**: Adequate resources should be allocated to support the post-mortem process, such as  tools, engineer who are trained to use the tools and reserve time for trying out Post Mortems.

To persuade Operations teams to start doing post-mortems after every production outage, you can highlight the following benefits:

1. **Learning from mistakes**: Post-mortems help teams identify and learn from their mistakes, improving their processes and systems.
2. **Preventing** future incidents: By identifying and addressing the root cause of an incident, teams can prevent similar incidents from happening in the future.
3. **Team building**: Post-mortems encourage collaboration and communication between different teams, improving team cohesion and effectiveness.

To persuade developers to join these post-mortems, you can emphasise that:
1. Developers play a crucial role in ensuring the stability and reliability of the system.
2. Post-mortems provide an opportunity for developers to learn from incidents and improve their code.
3. Developers can contribute to the remediation plan by suggesting improvements to the code or architecture.

Post-mortems are a critical tool for improving the reliability of software systems. By analyzing incidents, identifying the root cause of issues, and developing plans to prevent similar incidents in the future, teams can continuously improve the system's reliability and resilience. By establishing a blame-free culture, defining the scope and objective of the post-mortem, and communicating the benefits of the process to all stakeholders, you can successfully implement the Site Reliability Engineering (SRE) concept of a Blameless Post-Mortem.

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Supercharge your software operation with ZEN Software's Agile Analytics

What is agile?

Agile, Scrum, and Kanban are popular methodologies used in software development and project management. Each approach has unique characteristics, but all share a common goal: to make the development process more efficient and effective.


Agile methodology is a flexible and iterative approach to software development. It emphasizes collaboration, continuous improvement, and customer satisfaction. The Agile Manifesto states that working software is more important than comprehensive documentation and that responding to change is more valuable than [following a plan](https://agilemanifesto.org/).

Scrum is a framework within the Agile methodology. It emphasizes teamwork, accountability, and frequent progress checks. An organisation that employs Scrum knows the following roles: a Product Owner, a Scrum Master, and a Development Team member. Teams are autonomous and consist of 7 members (5 min, 9 max). The teams work in time-boxed iterations called **sprints**, with the goal of delivering a potentially shippable product increment at the end of each sprint. The scrum master guards the process, and the Product Owner decides what goes into a sprint and guides the teams in implementing his/her product vision.

Kanban is another framework within the Agile methodology, it stems from Lean and the Toyota Production System (TPS). It emphasizes visualization, workflow optimization, and continuous delivery. Kanban teams use a board with columns that represent different stages of work. Each task is represented by a card that moves from one column to the next as it progresses through the workflow. instead of sprint retrospectives like the ‘scrum-folk’ have: Kanban teams use Kaizen ('Good Change' from Japanese) sessions to reflect and improve on the process and outcomes.

Agile Analytics is a platform for optimizing software development. The platform allows teams to gain insights into their entire pipeline and integrates against all the tools used rather than being limited to Jira or Git. Agile Analytics integrates various systems like your project management system (Jira, Azure DevOps), monitoring systems (AWS CloudWatch, GCP Cloud Monitor, Azure Monitor), collaborative communication systems (Slack, Teams or Discord), and continuous delivery (like Github, Gitlab or Bit Bucket). Using this platform, teams can make more informed decisions and become data-driven! We’d love to show you how we can [supercharge your software operation](https://www.agileanalytics.cloud/request-demo)! 

In conclusion, Agile, Scrum, and Kanban are popular methodologies that are used in software development and project management. Agile is a flexible and iterative approach to software development, Scrum is a framework within the Agile methodology that emphasizes teamwork and accountability, and Kanban is another framework within the Agile methodology that emphasizes visualization and workflow optimization. These methodologies can be used separately or together depending on the project's needs. When the teams are working agile, measuring key metrics like productivity changes, our [next article shows you how to get insights into key metrics in an agile software development environment.](https://www.agileanalytics.cloud/blog/2023/4/how-to-measure-software-development-productivity)

Send us a message to request a demo or find out more.

How to measure Effectiveness of a DevOps Team

Do you ever find yourself wondering if your software development team is working as efficiently as possible? Measuring productivity in software development can be daunting, but ensuring your team is working at its full potential is critical. Luckily, we've got you covered. We'll explore concrete ways to measure software development productivity and the various metrics and tools that can be used to do so.

“development productivity painting“ generated by DALLE-2

Do you ever find yourself wondering if your software development team is working as efficiently as possible? Measuring productivity in software development can be daunting, but ensuring your team is working at its full potential is critical. Luckily, we've got you covered. We'll explore concrete ways to measure software development productivity and the various metrics and tools that can be used to do so. Get ready to dive into the world of Agile and SCRUM methodologies, DORA metrics developed by Google, Git merging and branching, machine learning, and project management tools like JIRA. And if that's not enough, we'll introduce you to Agile Analytics - a revolutionary software tool that combines all these metrics into one easy-to-read dashboard for organisations to make data-driven decisions and optimise their software development processes. Don't miss out on this game-changing information!

Agile and SCRUM methodologies are widely used in software development, and they emphasise continuous improvement and customer satisfaction. Organisations can use metrics such as velocity, burn-down charts, and cycle time to measure software development productivity using Agile and SCRUM. Velocity is the rate at which the team completes user stories or tasks, and it is measured in story points or hours. Burn-down charts show the progress of the team over time, and cycle time measures the time it takes to complete a user story or task from start to finish.

The DevOps Research and Assessment (or DORA) organisation is a research organization backed by Google that helps organisations improve their software development and delivery processes. DORA's research has identified four key metrics that organisations can use to measure software development productivity: **lead time**, **deployment frequency**, **mean time to recover** (MTTR) and **change failure rate** (CFR). Lead time measures the time it takes to go from code committed to code successfully deployed. Deployment frequency measures how often code is deployed to production. MTTR measures the time it takes to restore service when a failure occurs, and CFR measures the percentage of changes that result in a failure.

Merging and branching are critical aspects of software development, and Git (By Linux creator Linus Torvalds) is the most popular version control system that supports merging and branching. Organisations can measure software development productivity by measuring merge frequency and branch age. Merge frequency measures how often code changes are merged, and branch age measures how long branches remain open before merging or closing. Next to these base metrics: Agile Analytics supports the measurement of ‘software stock’: these are the changes and their risk category that are still ‘in progress’ and have yet to be delivered. Like ‘normal’ stock, software stock can be costly if not managed. Agile Analytics provides real-time insight into the current state of your software stock!

Machine learning is a technology already shaping many industries' future. ML can help your organisations optimize their software development productivity. By analyzing software development data, machine learning algorithms can identify patterns and make predictions about software development outcomes. For example, machine learning algorithms can predict which software development tasks are likely to take longer to complete or are more likely to result in failure.

In this example, Agile Analytics uses machine learning to determine whether a ticket is labelled Feature or Non-feature.

Sprint insights provided by Agile Analytics

Project management tools such as JIRA can also help organisations measure and optimize their software development productivity. JIRA provides a range of metrics, such as burndown charts, velocity, and cycle time, that can help teams monitor their progress and identify areas for improvement.

Measuring software development productivity is essential for optimizing software development. Organisations can use a range of metrics and tools such as Agile, SCRUM, DORA, Metrics, DevOps, Merging, branching, GIT, Machine Learning, Project Management, and JIRA to measure and optimize their software development productivity. By using these metrics and tools, organisations can identify areas for improvement and continuously improve their software development processes.

Agile Analytics is a software tool that combines all these metrics into one clear dashboard to help organizations address their data-related challenges. By leveraging Agile Analytics, organizations can improve their ability to make data-driven decisions, increase their agility, and optimize their software development process.

Overall, Agile Analytics is a powerful approach that can help organizations optimize their software development process and improve their data-related decision-making by embracing this methodology.

To learn more about how Agile Analytics works, watch our explainer video below.

How ChatGPT can enhance your software development team performance

Software development can be tough. With tight deadlines, many lines of code to write, and countless bugs to fix, it can feel like a never-ending uphill battle. But thankfully, ChatGPT is coming to the rescue to level up your team’s performance. Luckily ChatGPT is a much better coder than a poet.

Software development can be tough. With tight deadlines, many lines of code to write, and countless bugs to fix, it can feel like a never-ending uphill battle. But thankfully, ChatGPT is coming to the rescue to level up your team’s performance. Luckily ChatGPT is a much better coder than a poet.

ChatGPT is designed to generate natural language responses to user inputs, making it a very valuable tool in a wide range of applications, from language translation to chatbots. Now, let's dive into how your software development team can use ChatGPT to boost performance.

Collaboration is key to the success of any software development team. ChatGPT can be used to enhance collaboration by facilitating communication between team members.  ChatGPT allows team members to communicate effectively, provide real-time feedback on task progress, and collaborate on code and documentation. 

Using our AnalyticsBot as part of your Slack workspace, your teams can now employ chatGPT to suggest implementations or to help out with coding problems. Take this example, for instance:

By mentioning AnalyticsBot, Paul now has a solve for his coding problem.

ChatGPT can be used to improve code quality by analyzing code and identifying areas for improvement. 

ChatGPT can help you improve your code quality by providing a second set of eyes on your code, a.k.a code review. By submitting your code to ChatGPT, the language model can analyze the code and provide suggestions for improvements. For example, it can recommend better variable names, suggest ways to optimize code, or identify potential security vulnerabilities. 

It can also contribute to code refactoring by suggesting improvements to code snippets. ChatGPT can suggest ways to remove duplicate code, recommend better variable or function names, or identify areas where the code can be simplified.

ChatGPT can assist in staying up-to-date with the latest best practices in software development as well. By analyzing your code and comparing it to industry standards, ChatGPT can identify areas where your code can be improved to align with best practices. It can recommend the use of design patterns, suggest the use of automated testing, or highlight areas where code comments could be added etc.

With its ability to estimate project completion times and improve collaboration, ChatGPT is like the sidekick that helps developers become the superhero they always should have been. But like any good sidekick, ChatGPT comes with its quirks and unique personality. Sometimes it may be wrong or give an estimation that's a little off. So we have to be aware of it and not rely on AI completely.

This is where us mere mortals come in; Based on years of experience and with the right robotic tools, ZEN Software’s Agile Analytics and services will supercharge your software operation.

**Want to increase your software development team's productivity? Try Agile Analytics today with our 60-day free trial and watch your performance soar.**

 

Any questions? Contact us!

What is Agile Analytics?

Agile Analytics is our amazing platform for optimizing software operations. The platform allows teams to gain insights into their entire pipeline and integrates against all the tools used rather than being limited to Jira or Git. Agile Analytics integrates various systems like your project management system (Jira, Azure DevOps), monitoring systems (AWS CloudWatch, GCP Cloud Monitor, Azure Monitor),  collaborative communication systems (Slack, Teams or Discord), and continuous delivery (like Github, Gitlab or Bit Bucket). 

Agile Analytics is our amazing platform for optimizing software operations. The platform allows teams to gain insights into their entire pipeline and integrates against all the tools used rather than being limited to Jira or Git. Agile Analytics integrates various systems like your project management system (Jira, Azure DevOps), monitoring systems (AWS CloudWatch, GCP Cloud Monitor, Azure Monitor),  collaborative communication systems (Slack, Teams or Discord), and continuous delivery (like Github, Gitlab or Bit Bucket). Using this platform, teams can make more informed decisions and become data-driven!

How to measure productivity metrics of software teams

Understanding team performance is crucial for professionals in the tech industry, regardless of their role. It can be challenging to get a clear view of how teams are performing, but with the right tools and strategies, professionals can take their team's performance to the next level and achieve business goals more effectively. Practical tips include increasing team autonomy, balancing maintenance and feature work, and getting a grip on software development stock.

Assessing software development progress and its impact on production can be difficult, but there are tools and metrics available to gain a better understanding of team performance. Manually reviewing backlog items can provide insight, but automation is necessary for regular analysis. Objective metrics like Error Budgets can signal when to shift focus. The right metrics need to be identified to measure productivity accurately, such as DevOps DORA Metrics. Team culture is also important to consider, with tools like Kudos to gauge overall morale. Agile Analytics provides features to optimize workflows, measure team productivity using DORA metrics, and ensure a safe, fun, and engaging work environment.

['Error Budgets'](https://www.agileanalytics.cloud/features#errorbudgets) are a way to measure and manage the allowable amount of errors or downtime in a system or service. It involves setting a threshold for acceptable errors, and the remaining budget is used for making improvements or implementing preventative measures. This approach helps teams prioritize their efforts and resources to minimize the impact of errors and keep the system or service running smoothly.

Using Error Budgets, teams can automatically monitor their performance and receive alerts when their service level drops too low. This feature increases team autonomy and provides valuable insights into the quality of their service delivery.

- Monitors if your service level is inside or outside of 'budget'
- Improves the autonomy of your agile team
- Delivers actionable data about how you’re managing production

[Sprint Insights](https://www.agileanalytics.cloud/features#sprintinsights) is a feature that uses deep learning technology to determine whether a ticket represents **maintenance** or **feature work** and how much time is spent on each. This feature provides users with greater insight into their team's sprints, allowing them to make more data-driven decisions and focus on the areas that matter most. By analyzing millions of tickets from public sources, Sprint Insights provides accurate and comprehensive information, making it a valuable tool for teams looking to improve their productivity and efficiency.

- Creates insights in feature development balance
- Uses AI and machine learning to analyze your tickets
- Automatically registers the time spent on feature & non-feature work

[The Stocks](https://www.agileanalytics.cloud/features#stock) feature helps users manage their software development backlog, also known as their software stock. The Stocks feature in software development helps manage the backlog by tracking deployment cadence and identifying waste. It provides insights into the amount of work waiting to be released and helps identify bottlenecks and inefficiencies. This tool is helpful for improving the software delivery process, optimizing resources, and reducing costs associated with a high backlog. It shows unexecuted development changes, the productivity of the team, and relevant data about software stock and waste.

[The DORA metrics](https://www.agileanalytics.cloud/manage-devops-using-dora-metrics) are a set of performance metrics developed by Google's DevOps Research and Assessment team to help software development teams measure and improve their performance. These metrics are based on six years of research and are predictive of software delivery operational performance. The DORA metrics provide insights into successful deployments, end-to-end monitoring of feature realization, lead time for delivering value, service level, change failure rate, and operational productivity metrics. Teams can track these metrics continuously and automatically using the Agile Analytics platform. The DORA metrics feature is a valuable tool for any software development team looking to improve their performance and deliver better software faster. It provides clear and fair performance data, streamlines development processes, and creates happier developers while increasing engagement and motivation.

The Lost API Key feature in Agile Analytics is designed to help users scan their Git repositories for any lost API or private keys. If a lost key is found, users can quickly identify what happened, block the lost key, and resolve the incident, ensuring the safety and security of their systems.

This feature is particularly valuable for teams working on large software development projects where many keys may be in use, making it difficult to keep track of them all. By using Agile Analytics, users can quickly scan their repositories and identify any lost keys, preventing potential security breaches and ensuring the smooth operation of their systems.

- Get a clear insight into your teams’ software waste
- Figure out where software stock is created
- Automatically scan your Git repositories on ‘lost’ keys
- Easily mark the key and solve the incident

The Lost API Key feature is an important tool for teams looking to ensure the safety and security of their systems, particularly in high-risk environments where sensitive data may be at risk. With Agile Analytics, users can quickly identify and resolve incidents related to lost API or private keys, making it easier to maintain the integrity of their systems and safeguard their data.

The hour registration feature in the Agile Analytics platform allows users to quickly and easily register the number of hours worked. The platform also includes an inferred hours feature that suggests the number of hours worked based on the user's input. This all-in-one platform feature can help users keep track of their work hours accurately and efficiently.

The feature allows users to reward their team members with Kudos and celebrate their personal and team achievements. It also provides leaderboards to keep track of scores and encourage friendly competition. Overall, the feature promotes a fun and positive team dynamic and encourages collaboration and recognition among team members.

- Celebrate team achievements together.
- Reward each other with Kudos
- Have a little fun and recognize each other! (:balloon: , :star:, :+1:)

Are you tired of guesswork and unproductive software development cycles? Look no further than Agile Analytics! By integrating with all existing support tools, our platform provides critical insights into the health of your software operations and where to improve. With features like Error Budget, teams will know exactly when to push forward on feature development or break a sprint. Our Deep Learning-powered dashboards empower software teams with the right metrics to become high-performing, all while reducing defects in production and cutting feature delivery time. 

**Say goodbye to unproductive cycles and hello to Agile Analytics!**

Visit our [website](https://www.agileanalytics.cloud/) or contact us at <hello@agileanalytics.cloud> to learn more.

Agile doesn't work...

> Agile doesn't work...

I hear it more and more.
When I use root cause analysis to find out what the problem is, it's usually due to a combination of:

1. An Agile implementation that's not very agile.
2. No metrics in place to measure success or improvements.

> Agile doesn't work...

I hear it more and more.

When I use root cause analysis to find out what the problem is, it's usually due to a combination of 
1. An Agile implementation that's not very agile.
2. No metrics in place to measure success or improvements.

Most organisations who ‘go agile’ forget the Agile [values](https://agilemanifesto.org/):

1️⃣ “Responding to change over following a plan.”   
2️⃣ “Working software over comprehensive documentation.”  
3️⃣ “Customer collaboration over contract negotiation.”  
4️⃣ “Individuals and interactions over processes and tools.”  

These agile values apply to how you implement Agile as well.

Don't put the scrum guide, SAFe Consultants or any other process or tools over your team and their interactions.

It means that it is both:
- a balancing act, ‘do the first thing and don’t forget the second thing.'
- and continuously improve both the product and the processes you are a part of. (retrospective!)

It means you should write **documentation**, but it is not your team or task's critical, singular deliverable.

It means you should write **good user stories** but start with documenting the Who, Why, What, and How and move on from there.

However, like any approach, agile is not a one-size-fits-all solution and may not turn out the way you want it if you ‘forget’ the agile values it is based on while implementing it. 

A great way to continuously check how the organisation is doing is to use metrics and KPIs based on the [agile manifesto](https://agilemanifesto.org/). Let’s go over them one by one:

1️⃣ **“Responding to change *over* following a plan.”**

Insight into the feature development balance allows you to react to a change in focus and steer the backlogs and teams when needed. This is how that works:

When working in a sprint (or in the continuous flow of kanban), you must, at some point, know what the ‘type' of work is that your teams are working on. How much time is spent on feature development, and how much is spent on maintenance or non-feature development? Agile Analytics' [Sprint Insights](https://www.agileanalytics.cloud/features#sprintinsights) feature is a deep learning A.I.-based technology that will measure and report on the balance between Feature Development and non-feature development. It works by categorising (using deep learning) each update to tasks in a project management system like Jira. It will report how much **focus was on feature development vs non-feature development**. Also, you can see how much **time was spent** on each of these categories. 

2️⃣ **“Working software *over* comprehensive documentation.”**

By measuring the SLOs of the ‘working software’, you will have instant and constant feedback about your production state and if the teams are focussing on ‘the right things'.

Using the SRE practice of [Error Budgets](https://www.agileanalytics.cloud/features#errorbudgets) and the Agile Analytics' unique feature of [Software Stock](https://www.agileanalytics.cloud/features#stock), you can now keep track of the continuous ‘working software’ by measuring SLOs 24/7 and being alerted in real-time when an Error Budget runs out! This gives the teams the empowerment and autonomy to respond to service-level changes that warrant a refocus of the work.  

Additionally, by measuring how much software is **‘in stock’**, as opposed to delivered to production, you make sure the operation is continuously optimised to provide a constant stream of working software as fast as possible. Measuring this is critical to objectively measure the software organisation's agility: Using ZEN Software’s [Agile Analytics Software Stock feature](https://www.agileanalytics.cloud/features#stock), you will be able to do that!

3️⃣ **“Customer collaboration *over* contract negotiation.”**

By working closely together with your customers and being transparent about service level and productivity using the industry-standard [DORA Metrics](https://www.agileanalytics.cloud/manage-devops-using-dora-metrics). You will quickly build trust and close collaboration with your customer(s).

Additionally, the [DORA Metrics](https://www.agileanalytics.cloud/manage-devops-using-dora-metrics) allow you to optimise internally to value the quick response to failed deployments and a cycle time that is no longer than the industry standard.

4️⃣ **“Individuals and interactions *over* processes and tools.”**

By measuring the atmosphere on communication platforms like Slack, Teams or Discord, you’ll be able to see if teams are celebrating successes and rewarding good behaviour. 

Using Agile Analytics' amazing [Kudos Feature](https://www.agileanalytics.cloud/features#kudos), you’ll be able to see using leaderboards who tops the charts and who awarded who with a Kudo.

So while some of the teams that utter “Agile does not work” are snowflakes bemoaning how special they perceive themselves to be. Sometimes it is because of a bad implementation that takes the processes too literally. An Implementation of Agile that really ‘does not work’ causes an organisation to (dis)function identically. However, now all the non-agile processes contain agile labels; this is referred to as Agile In Name Only (or label-writer-agile)

When you do not measure the ‘before’ and ‘after’ situation using relevant metrics, it remains a best guess as to how the performance improves. Using our amazing platform Agile Analytics, you’ll be able to measure all the critical aspects based on the Agile Manifesto for your agile software development organisation. You can now objectively measure whether the teams are improving or needing attention.

Increase Engineer Engagement by measuring what matters

Embracing Error Budgets: A Guide to Implementing SRE in Your Organisation

Error budgets are an essential tool for Site Reliability Engineering (SRE) that allow organisations to balance reliability and innovation. In this post, we will discuss how to introduce the practice of Error Budgets in your organisation and make the necessary changes to support it.

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