Automate your non-functional quality management
Software quality goes beyond functionality. Non-functional aspects like performance, reliability, and security are crucial. By leveraging DORA metrics, SRE Error Budgets, Sprint Insights and Software Stock teams can optimise deployment, reliability, and security.
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.
What are the DORA Metrics?
Deployment Frequency (DF)
Refers to the frequency of successful software deployments to production. By measuring deployment frequency, the organisation measures the cadence of the deployment work.
Operational productivity metric
Insights into successful deployments
Track your Software Delivery Cadence
Lead Time for Changes (LT)
Captures the time between a code change commit and its deployable state. This metric is critical for determining the flow of work.
From idea to realisation
End-to-end monitoring on feature realisation
Track lead time for delivering value
Mean Time to Recovery (MTTR)
Measures the time between an interruption due to deployment or system failure and full recovery.
How fast are your teams recovering from incidents?
Insights into the Service Level, Too high? too low?
Change Failure Rate (CFR)
Indicates how often a team’s changes or hotfixes lead to failures. This metric indicates the defects that occur when developing software.
Detect and remove brittle deployment procedures
Are production incidents caused by bad deployments?
See if tests have sufficient coverage and maintain production service level
DORA metrics enable the measurement of team productivity through quantitative indicators like Deployment Frequency and Lead Time for Changes. These metrics allow organisations to track and compare productivity levels, identify bottlenecks, and drive improvement efforts. Additionally, DORA metrics 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, organisations can identify high-performing teams that consistently deliver value while upholding high-quality standards.
Setting up the process of Error Budgets empowers your teams by granting them increased autonomy in decision-making regarding feature development and quality aspects. Error Budgets 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 prioritise their efforts effectively.
Using Agile Analytics' Sprint Insights, 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 analyse and categorise tasks, allowing teams to understand how their time is being allocated and make data-driven decisions. This knowledge empowers teams to prioritise effectively, optimise 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,' 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 minimise the risk of encountering unstable conditions during the software's release to production.
Leaked Private Keys
Looking for Private Keys in your internal source control system is crucial to ensure that unauthorised access to protected cloud resources is prevented within your organisation. 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, 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 organisation's data and systems.
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At ZEN Software, we are ready to assist you with Agile Analytics
Clear and fair performance data about your software development team
Automated management and measuring of non-functional quality metrics like performance, security and reliability
Make Agile teams happier while increasing engagement and motivation