How can you use QA automation metrics to monitor software development lifecycle changes?

2 What QA automation metrics to use?

When it comes to QA automation metrics, there are many options depending on your goals, context, and tools. Test execution metrics measure how many tests are executed, how often, how long they take, and what the outcomes are. Test quality metrics indicate the effectiveness of tests in finding and reporting defects, as well as how quickly they are resolved. Test coverage metrics assess how well tests cover code and requirements, and how many test cases are mapped to each one. Finally, test efficiency metrics measure the utilization of testing resources and tools, time and cost savings from automation, and reduction of manual testing efforts.

4 How to analyze QA automation metrics?

To analyze QA automation metrics, you need to have a clear and relevant set of criteria and benchmarks for evaluating your test performance and quality. Various techniques and methods can be used to achieve this, such as trend analysis, which involves comparing test data and metrics over time to identify patterns, changes, and anomalies. Additionally, root cause analysis involves investigating the underlying causes of test failures, defects, and issues. Gap analysis is another technique that compares test data and metrics with expected or desired outcomes, standards, or best practices. Lastly, cost-benefit analysis weighs the costs and benefits of your test automation efforts and investments.

5 How to use QA automation metrics to monitor software development lifecycle changes?

To use QA automation metrics to monitor software development lifecycle changes, you need to have a flexible and agile approach to testing that can adapt to the evolving needs and requirements of your software project. This can be achieved through various practices and principles, such as continuous testing, which involves integrating testing activities and feedback into the delivery pipeline, test-driven development, which involves writing test cases before code to meet requirements, shift-left testing, which involves moving testing activities earlier in the SDLC, and shift-right testing, which involves extending testing activities beyond the SDLC to monitor and improve performance in production.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?