A Learning Classifier System for Automated Test Case Prioritization and Selection

Rosenbauer, Lukas; Pätzel, David; Stein, Anthony; Hähner, Jörg

doi:10.1007/s42979-022-01255-1

Cited by 3 publications

(1 citation statement)

References 32 publications

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“…There has been a lot of research on test case prioritization in continuous integration environment. Lima et al [16] improves COLEMAN, a learning-based sorting method, and puts forward two strategies to deal with variables, which makes COLEMAN practicable for highly-configurable software in continuous integration; Rosenbauer et al [17] proposed and optimized a test case ranking model based on Learning Classification System (LCS), and demonstrated through experiments that it performs better than network-based models; Xiao et al [18] proposed a sorting method based on Long Short-Term Memory Network (LSTM) and applied it to embedded software, which improved its fault detection rate in the continuous integration environment; Ali et al [19] proposed a clustering method that clusters and sorts test cases based on their historical failure frequency and coverage criteria, achieving a defect detection rate of over 90%.…”

Section: Introductionmentioning

confidence: 99%

An Improved Method for Test Case Prioritization in Continuous Integration based on Reinforcement Learning

Han,

Chen,

Han

2023

Atlantis Highlights in Engineering

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The iterative update of software leads to frequent continuous integration, so the testing in the continuous integration environment should also be fast and accurate. Reinforcement learning is often used in the research of continuous integration testing because of its sequential strategy and good robustness. Some existing methods use reinforcement learning to solve test case prioritization problem, which provides a good idea, but the experimental defect detection rates are relatively low. Therefore, based on the existing reinforcement learning framework, this article proposes a reward mechanism to provide additional rewards for newly emerging test cases in each integration cycle. Through experiments on three industrial datasets, it has been proven that this mechanism improves the defect detection rate, the recall rate of failed test cases, and the efficiency of test feedback in the testing process.

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