mlsanityData to 7 Oct 2026

A Comparison of Reinforcement Learning Frameworks for Software Testing Tasks

arXiv 2208.12136 ↗First posted 25 Aug 2022cs.SE cs.LGcited by 3

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Abstract (arXiv)

Software testing activities scrutinize the artifacts and the behavior of a software product to find possible defects and ensure that the product meets its expected requirements. Recently, Deep Reinforcement Learning (DRL) has been successfully employed in complex testing tasks such as game testing, regression testing, and test case prioritization to automate the process and provide continuous adaptation. Practitioners can employ DRL by implementing from scratch a DRL algorithm or using a DRL framework. DRL frameworks offer well-maintained implemented state-of-the-art DRL algorithms to facilitate and speed up the development of DRL applications. Developers have widely used these frameworks to solve problems in various domains including software testing. However, to the best of our knowledge, there is no study that empirically evaluates the effectiveness and performance of implemented algorithms in DRL frameworks. Moreover, some guidelines are lacking from the literature that would help practitioners choose one DRL framework over another. In this paper, we empirically investigate the applications of carefully selected DRL algorithms on two important software testing tasks: test case prioritization in the context of Continuous Integration (CI) and game testing. For the game testing task, we conduct experiments on a simple game and use DRL algorithms to explore the game to detect bugs. Results show that some of the selected DRL frameworks such as Tensorforce outperform recent approaches in the literature. To prioritize test cases, we run experiments on a CI environment where DRL algorithms from different frameworks are used to rank the test cases. Our results show that the performance difference between implemented algorithms in some cases is considerable, motivating further investigation.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 27 of its 60 references that are in the map. First posted on arXiv 25 Aug 2022.

Builds on

27 of 60 references.

References · 27

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15 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2026A Reference Architecture of Reinforcement Learning Frameworkscited by 3
  2. 2025On the Mistaken Assumption of Interchangeable Deep Reinforcement Learning Implementationscited by 0
  3. 2025CiRL: Open-Source Environments for Reinforcement Learning in Circular Economy and Net Zerocited by 0