Test case prioritization is frequently used as an approach for managing software regression testing. The purpose of regression testing is to ensure new changes or bug fixes have not broken the existing functionality in the application. Many QA testing teams find themselves unable to execute all possible tests due to time and resource constraints. Why? Largely because regression test suites grow exponentially depending on the application complexity and the number of features released. It’s like standing frozen at the bottom of a hill watching a snowball gather size and speed as it rolls downhill towards you.

Test case prioritization (TCP) is a method of managing regression testing. The idea is to group tests that pose the greatest risk to the software quality into test suites and use them for regression. TCP replaces the need to test all possible tests while still covering the high-risk areas of the application. Using TCP improves software testing efficiency without negatively impacting application quality.

This guide describes how data-driven analytics improve TCP practices to improve application quality and increase QA testing efficiency.

What does “Data-Driven” Mean?

Data-driven TCP means leveraging testing data and metrics to establish test prioritization rules. Instead of depending on QA tester experience, developer input, or non-data-based decisions on test case prioritization, real data is analyzed and used instead.

The beauty of using data-driven analytics to determine test case priority is the sheer accuracy. The data used is the data from previous application test results, defect history, and code complexity analysis. Analyzing real data improves the accuracy of regression testing management.

Why is Test Case Prioritization Important in Testing?

Ross Collard in “Use Case Testing” states that: “The top 10% to 15% of the test cases uncover 75% to 90% of the significant defects.”

Test case prioritization will help make sure that these top 10% to 15% of test cases are identified. TCP increases both the accuracy and timeliness of regression testing. Modern software development teams struggle constantly with balancing application quality and speed of delivery. Both are critical to the business. Application quality ensures customers use the application and recommend it to others. While the speed of delivery helps businesses to stay competitive in a rapidly changing business market.

TCP also allows QA testing teams to efficiently manage an ever-growing regression testing suite without compromising quality. Additionally, TCP provides effective test coverage when test execution time is short or testing resources are limited. Hence the power of leveraging data-driven analytics to build prioritized test case suites.

TCP improves testing by:

Reducing the number of test cases to execute.
Building prioritized test case suites based on data-driven analysis keeps test coverage aimed at the riskier areas of the application.
Helps to keep development releases both on time and with high quality.
Executing tests early and often improves bug identification early in the development cycle.
Provides effective risk-based prioritization test case execution.
Makes history-based prioritization extremely precise when determined by analyzing the application’s data.

Risk-based prioritization with data-driven analytics determines which areas of the code carry the most risk of causing a defect. Test cases deemed high risk are listed as a higher priority and executed during regression testing.

History-based prioritization is used as a secondary method of TCP for regression testing. For history-based prioritization, data is analyzed for the history of defects and fault detection rates. Test cases with a higher failure rate are prioritized higher and executed during regression testing. Both risk and history-based prioritization are similar means of determining risk. Selecting one option provides QA testing teams with the means for accurately performing TCP.

How can Analytics Play a Role in Effective Test Case Prioritization?

Data-driven analytics play a crucial role in the quality of short or rapid regression testing practices. By using real application and team data, QA testing teams leverage the value of prioritizing tests based on data, or facts.

The metrics that form the analytics for data-driven TCP include:

Defect detection rate across the application
The number of defects per requirement or user story
Regression test execution length or time history

Keep in mind, the quality of the data used to gather metrics and analytics is critical. Use a combination of test metrics for improved data accuracy. Analysis metrics can help testing teams focus testing on problem areas and improve test execution speed. Adjust your regression testing suites based on the results of analytics. Don’t stop there, consider using a wide variety of test analytics for greater accuracy and analytics quality.

What are the Key Analytics for Determining Test Case Prioritization?

The key analytics crucial for determining Test Case Prioritization include:

Predictive Analytics: These analytics employ existing data to forecast potential issues. Typically found within artificial intelligence (AI) and machine learning (ML) tools, they help in identifying patterns of failure.
Defect Density per Application Function: This metric involves numerical data on defects identified within each application function or functional area. It provides insights into the reliability of different aspects of the application.
Defect Density per Customer Workflow: This analytics metric focuses on the number of defects identified within complete end-to-end customer workflows. It’s essential for understanding how well the software serves users.
Change Frequency: Change frequency data relates to both the rate of changes within the code and the associated test cases. Frequent code changes often necessitate adjusting test priorities.
Test Flakiness Index: Flaky tests, which inconsistently pass and fail, are tracked using this index. It helps identify tests that need attention due to their inconsistent behavior.
Failure History Data (FHD): Leveraging historical data on failed test cases, FHD enables the organization of test cases from the highest to lowest failure rate. ML can also make use of FHD to automatically reprioritize tests with each regression run. This ensures that tests adapt to evolving software conditions and remain focused on the most critical areas.

The key analytics used for TCP depend on the application maturity and the amount of tracked defect data. For development teams that don’t retain defect data or record test execution results, data-driven analysis for these two analytics is not possible. Use others or use defect data per sprint or development cycle instead.

Organizations can tweak the key analytics used based on their operations. Many Agile teams do not track data history, which may make data-driven analytics for TCP challenging. However, be creative and use the team to help determine what data can be used for TCP evaluation.

Also, review your development and test team tools. Many test management and developer tools include built-in analytics that can be effectively leveraged for TCP.

Is Your Testing Team Looking to Increase Test Effectiveness?

Using data-driven TCP analytics and test metrics improves the accuracy of test prioritization. TCP created from real application and development team data improves the accuracy of TCP and is not subject to bias or habit. In today’s modern software testing teams, test execution speed must be constantly balanced with application quality.

If your testing team consistently runs short on regression test execution time or is not even currently performing regression testing then consider using available testing metrics and analytics. Leveraging analytics helps testing teams reduce the size of regression testing suites and helps keep tests prioritized based on risk and defect occurrence.

Start with analytics by selecting one or two metrics or test analytics. See how it helps your testing efficiency and effectiveness. If possible, expand to using additional test metrics and harness the power of both AI and ML where it’s useful. Use analytics to trim test execution time while also making it more effective and focused. Keep the balance equal between speed and quality for the best business results.

Regardless of your test prioritization strategy, it’s vital to validate your tests in real user conditions for improved accuracy. Utilizing a real device cloud, such as LambdaTest, expands your test coverage with access to over 3000 real browser-device combinations.

This approach accelerates and enhances software testing, ensuring a faster and more precise evaluation. Utilize LambdaTest AI-powered test analytics and observability to identify critical tests, reduce the size of regression test suites, and improve the accuracy of test prioritization.

A Data-Driven Approach to Test Case Prioritization: The Role of Analytics