Adaptive Reward Computation in Reinforcement Learning-Based Continuous Integration Testing

Adaptive Reward Computation in Reinforcement Learning-Based Continuous Integration Testing

Reinforcement learning (RL) has been applied to prioritizing test cases in Continuous Integration (CI) testing, where the reward plays a crucial role. It has been demonstrated that historical information-based reward function can improve the effectiveness of the test case prioritization (TCP). Howev...

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Bibliographic Details
Main Authors: Yang Yang, Chaoyue Pan, Zheng Li, Ruilian Zhao
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Continuous integration
test case prioritization
reinforcement learning
sliding window
reward computation
Online Access:https://ieeexplore.ieee.org/document/9366947/

Internet

https://ieeexplore.ieee.org/document/9366947/

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