A Scalable Automated Regression Testing Approach using Data Mining Techniques

Abstract

Regression testing repeatedly executes test cases of previous builds to validate that any new changes occurred did not affect the original features. It is the type of software testing that seeks to uncover new software bugs in existing areas of a system after changes have been made to them. In recent years, regression testing has seen a remarkable progress with the increasing popularity of agile methods, which stress on the central role of regression testing in maintaining the software quality. The significance of regression testing has grown with the amplified adoption of agile development methodologies. The optimum case for regression testing in agile context is to run regression set at the end of each sprint and release, which requires a lot of cost and time.

In this master’s thesis, we present an automated scalable agile regression testing approach on both the sprints and release levels. As for the sprints level, the proposed approach addresses weighted sprint test cases prioritization technique (WSTP) that prioritizes test cases based on several agile parameters having real practical weight for testers. Regarding the release level, two different approaches are proposed: 1. Cluster-based Release Test cases Selection technique (CRTS), which clusters user stories based on the similarity of covered modules to solve the scalability issue. Test cases are then selected based on issues logged for failed test cases using text-mining techniques