Running all regression tests all the time can be impractical and inefficient
DevOps and continuous deployment represent a paradigm shift in software development, promoting collaboration, automation, and a continuous feedback loop. These practices are instrumental in meeting the demands of a dynamic and competitive market, allowing organizations to deliver high-quality software quickly and efficiently.
In this context, software testing has undergone a transformation to align with the accelerated pace of development methodologies, emphasizing the roles of test automation and continuous testing practices. However, with the increasing complexity of applications, the number of automated tests tends to grow exponentially. This growth results in prolonged execution times even for automated tests, sometimes spanning hours or days, contradicting modern development practices that prioritize quick feedback loops.
This presents a unique challenge for testing teams: the necessity to strategically select and prioritize tests due to resource constraints and time limitations. The sheer volume of tests can lead to a tipping point where it becomes impractical to execute every test in every regression cycle.
The team is then confronted with the critical task of identifying and focusing on high-priority test cases, employing some type of prioritization criteria, and continually maintaining and updating the test suite to ensure relevance and effectiveness within the context of the adopted criteria.
Strategies for selecting and prioritizing test cases
Considering the trade-offs between comprehensive automated regression testing and more focused partial regression, the crucial challenge is to methodically select and prioritize test cases within the test suite, aiming to strike a delicate balance between comprehensive coverage and the effectiveness of the testing process while being mindful of resource limitations.
In the list below, you'll discover various methods to prioritize test cases, all intended to strike a balance between comprehensive coverage and reducing execution time. Keep in mind that this list is not exhaustive:
In this article, our goal is to examine how the integration of business impact and frequency of use methods can lead to a more comprehensive approach to test prioritization. By merging these approaches, testing teams can devise a prioritized test plan, optimizing the efficacy of their testing efforts and enhancing the overall quality of the application under test.
Combining the business impact and frequency of use to prioritize test cases
Often, teams find themselves struggling with the challenge of quantifying the business impact and frequency of use, resorting to an intuitive and subjective approach. To elevate confidence levels, it becomes imperative for teams to transition towards a more formalized methodology.
This involves the development of a systematic scoring system that integrates both the business impact and frequency of use for the application's functionality. This formalized approach empowers teams to prioritize test cases more effectively based on these crucial factors, minimizing reliance on guesswork.
Here's a step-by-step guide to prioritize tests using these factors:
- Identify critical business requirements and features: Review the business requirements and features of your application or system. Identify those with the most substantial impact on business operations, customer satisfaction, or revenue.
- Assess frequency of use: Analyze usage data or gather stakeholder feedback to pinpoint the most frequently used features or functionalities in production.
- Create a prioritization matrix: Develop a matrix that blends both business impact and frequency of use factors. Assign weights or scores based on their relative importance to your company.
- Prioritize test cases: Map your test cases to the corresponding requirements or features. Calculate a priority score for each test case by combining the scores or weights assigned to the business impact and frequency of use factors.
By following this approach, you can focus your testing efforts on the most business-critical and frequently used aspects of your application or system while ensuring a well-rounded test coverage.
Gravity: Applying AI for test case prioritization
Gravity introduces an AI-powered test case weighting & scoring engine that helps optimize existing test suites by prioritizing test cases based on the business impact and frequency of use in the tested pages and flows covered by these test cases. This enables a data-driven test case prioritization, focusing test coverage on high-impact areas that directly affect the end-user experience.
It seamlessly correlates multiple dimensions, including test coverage, business impact, and frequency of use. This enables the generation of easily understandable reports and insights, empowering testing teams to prioritize tests based on concrete data rather than relying on guesswork.
Gravity's ability to monitor usage within the production and testing environments allows it to generate comprehensive quality analytics by processing the ingested data through machine learning algorithms. This involves translating raw data into meaningful insights using techniques such as pattern recognition, trend and correlation analysis, anomaly and outlier detection, and more.
It highlights information that allows testing teams to identify gaps in coverage, pinpoint features that may be either over-tested or under-tested, and recognize redundant testing efforts in less critical areas.
If you want to learn more about Gravity, you can book a demo here: Book a demo
Conclusion
Embracing a focused and selective test case prioritization approach is crucial for optimizing testing resources, accelerating time-to-market, and elevating overall software quality. By directing efforts toward high-risk areas of the application, testing teams can ensure the early discovery of critical issues.
Additionally, the adoption of a formalized scoring system for prioritizing relevant test cases provides a considerable benefit for testing teams. This systematic method represents a significant improvement over relying solely on subjective opinions and the experiences of others. By employing a more objective and data-driven methodology, testing teams can significantly enhance their testing practices, ensuring a more rigorous and effective approach to identifying and addressing critical issues.
Author: Cristiano Caetano
Head of Growth at Smartesting
Software testing authority with two decades of expertise in the field. Brazilian native who has called London home for the past six years. I am the proud founder of Zephyr Scale, the leading Test Management application in the Atlassian ecosystem. Over the last ten years, my role has been pivotal in guiding testing companies to build and launch innovative testing tools into the market. Currently, I hold the position of Head of Growth at Smartesting, a testing company committed to the development of AI-powered testing tools.