Test Suite Prioritization Based on Optimization Approach Using Reinforcement Learning

Abstract

Regression testing ensures that modified software code changes have not adversely affected existing code modules. The test suite size increases with modification to the software based on the end-user requirements. Regression testing executes the complete test suite after updates in the software. Re-execution of new test cases along with existing test cases is costly. The scientific community has proposed test suite prioritization techniques for selecting and minimizing the test suite to minimize the cost of regression testing. The test suite prioritization goal is to maximize fault detection with minimum test cases. Test suite minimization reduces the test suite size by deleting less critical test cases. In this study, we present a four-fold methodology of test suite prioritization based on reinforcement learning. First, the testers’ and users’ log datasets are prepared using the proposed interaction recording systems for the android application. Second, the proposed reinforcement learning model is used to predict the highest future reward sequence list from the data collected in the first step. Third, the proposed prioritization algorithm signifies the prioritized test suite. Lastly, the fault seeding approach is used to validate the results from software engineering experts. The proposed reinforcement learning-based test suite optimization model is evaluated through five case study applications. The performance evaluation results show that the proposed mechanism performs better than baseline approaches based on random and t-SANT approaches, proving its importance for regression testing.

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