Book a demo

Features

Pricing

About us

Blog

Login



Dark, powerful font logo communicates brand: "AGILE Analytics, Quant

1080_logo_agile_analytics_black.webp

1200_logo_agile_analytics_black.webp

1920_logo_agile_analytics_black.webp

2048_logo_agile_analytics_black.webp

256_logo_agile_analytics_black.webp

384_logo_agile_analytics_black.webp

640_logo_agile_analytics_black.webp

750_logo_agile_analytics_black.webp

828_logo_agile_analytics_black.webp

thumbnail_logo_agile_analytics_black.webp

font-logo-graphics-brand-signage-darkness-rectangle-photo-caption

Logo Agile Analytics

Subscribe

Subscribe to Agile Analytics' newsletter

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

256_2b56a95a-a511-4739-b612-675be61ecf91.webp

384_2b56a95a-a511-4739-b612-675be61ecf91.webp

640_2b56a95a-a511-4739-b612-675be61ecf91.webp

thumbnail_2b56a95a-a511-4739-b612-675be61ecf91.webp

2b56a95a-a511-4739-b612-675be61ecf91.webp

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.

e011c4af-9c01-4858-a4b0-9cee8d00e1c3.webp

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

 

Explore Agile Analytics

Get free 60-day Agile Analytics trial

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

256_preview-excel.webp

384_preview-excel.webp

thumbnail_preview-excel.webp

preview-excel.webp

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

analytics-preview.webp

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.

7bc8a18c-9d31-41d7-a223-4006ab8dbaaa.png

**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.

a771eebe-a478-471d-be80-957963a49282.webp

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.

bc496e52-0393-4c43-b305-82b2d5c6b702.png

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!

cta_background.webp

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

256_image (17).webp

384_image (17).webp

thumbnail_image (17).webp

image (17).webp

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.

4c1f5ea9-347f-43c1-b045-e328c8410c17.webp

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?

256_4a6fb5b0-5078-4646-98ed-190610494f9f.png

384_4a6fb5b0-5078-4646-98ed-190610494f9f.png

640_4a6fb5b0-5078-4646-98ed-190610494f9f.png

thumbnail_4a6fb5b0-5078-4646-98ed-190610494f9f.png

4a6fb5b0-5078-4646-98ed-190610494f9f.png

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

bc496e52-0393-4c43-b305-82b2d5c6b702.webp

57397db8-c5ad-4d32-9484-e09780afc269.png

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.

e0e004b1-4ba1-49b4-bc5a-3593270ab8de.png

Setting up a Latency SLO in Agile Analytics is super easy

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

c764af50-9dff-47cf-b13e-d0094ef80872.webp

**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

256_preview-pm.webp

384_preview-pm.webp

thumbnail_preview-pm.webp

preview-pm.webp

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.

30b90b0c-d6ba-46a2-8c3c-0e584198852a.webp

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.

e19ae0c1-951d-4050-a153-d35f2a566682.webp

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.

e4767fb0-d3a9-4e98-be27-dd6f5e0c24a7.webp

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.

c9592a61-432b-465a-bee4-b8b415655805.webp

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.

2d0ff381-bb8c-48fe-8da7-025a66a8dff0.webp

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



Logo of defunct Code Spaces, previously part of Cadbury.

256_cadbury-logo

384_cadbury-logo

640_cadbury-logo

thumbnail_cadbury-logo

cadbury-logo

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.



Computer-monitor w/ "Code Spaces" text; peripheral to personal

computer-personal-computer-output-device-peripheral-computer-monitor-font-gadget-computer-monitor-accessory

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.



Electric-blue logo w/"Code Spaces" in font rectangle: brand

font-rectangle-logo-brand-electric-blue-graphics-peach-symbol

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!



Cartoon verts frm fun bench: Creative art outdoors - "

256_vertebrate-cartoon-bench-art-creative-arts-illustration-outdoor-bench-outdoor-furniture

384_vertebrate-cartoon-bench-art-creative-arts-illustration-outdoor-bench-outdoor-furniture

640_vertebrate-cartoon-bench-art-creative-arts-illustration-outdoor-bench-outdoor-furniture

750_vertebrate-cartoon-bench-art-creative-arts-illustration-outdoor-bench-outdoor-furniture

828_vertebrate-cartoon-bench-art-creative-arts-illustration-outdoor-bench-outdoor-furniture

thumbnail_vertebrate-cartoon-bench-art-creative-arts-illustration-outdoor-bench-outdoor-furniture

vertebrate-cartoon-bench-art-creative-arts-illustration-outdoor-bench-outdoor-furniture

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.



Cartoon Organism overcoming World through Adaptive Art Fictional-character

cartoon-organism-world-adaptation-art-fictional-character-fiction-font

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.



Azure-slope-rectangle-triangle plot with text

product-azure-slope-rectangle-triangle-plot-font-line

**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.



IFS logo: Agile analytics, Error Budgets, DOR

institute for fiscal studies-logo

**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.



Analytic insights to optimize stats for agile Institute for Fiscal Studies.

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



Happy Fictional-character making a thumb-up gesture with LEGO:

256_image (7).webp

384_image (7).webp

640_image (7).webp

thumbnail_image (7).webp

smile-happy-gesture-finger-font-thumb-lego-fictional-character

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



Logo of Institute of Fiscal Studies for analytics, error budgets, D

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.



Atmosphere of azure electric sky, with water, slope,

atmosphere-water-sky-azure-electricity-astronomical-object-slope-electric-blue

Start free trial

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



Creative hairstyle, with shoulder shrug, tartan waist and sleeve

256_get-more-features-preview.png

384_get-more-features-preview.png

640_get-more-features-preview.png

thumbnail_get-more-features-preview.png

hairstyle-shoulder-facial-expression-tartan-neck-sleeve-gesture-waist

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.



Girl smiles in tartan fashion, subtly accessorised with eyew

hair-shoulder-facial-expression-fashion-tartan-sleeve-eyewear-waist

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



Logo of Institute Fiscal Studies for agile analytics & insights, sprints

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.



Logo w/ text for agile analytics org to conn w/ J

Recent Blogs

We’re confident we can supercharge your software operation.