Reinforcement Learning for Dynamic Test Execution Planning in Continuous Integration Pipelines

Abstract

Software development nowadays leverages CI/CD pipelines. Code changes may be made quickly without harming program quality. Test execution becomes harder as pipelines grow, especially in big systems with numerous test cases. Test scheduling traditionally slows builds and uses resources. In dynamic CI/CD systems, reinforcement learning (RL) may improve test scheduling and prioritisation. Automatic test selection utilising reinforcement learning (RL) improves CI pipeline speed, reliability, and efficiency. 

Reinforcement learning agents target environmental rewards. Select RL-CI test agents. Resource, past findings, and big bug probability prioritise testing. Smart RL optimises testing using past runs and system performance. 

Understanding the environment and reward function is difficult when using RL for dynamic test execution planning. CI covers tests, computation, codebase status, and problems. Agents must prioritise speed, coverage, and errors. Create and test prizes. Save time, assure coverage, and prioritise tests with a higher failure risk due to history or codebase. 

Policy Gradient, Deep Q Networks, and Q-learning may help. Each solution's benefits depend on CI ecosystem size and complexity. Q-learning and its variants can work in basic CI with restricted state space and predictable system activity. Large systems with many test cases and non-linear code and test changes may necessitate DQN or Policy Gradient. Addressing CI trade-offs and uncertainty, these strategies help the agent learn complicated rules that enhance test performance over time. 

Real-time decision-making and model training power affect CI pipelines. RL agents need plenty of data and computation, making training hard. RL agents must fast adapt to CI pipeline codebase, test suite, and resource changes. Classifying test cases or identifying anomalies using RL and supervised or unsupervised machine learning may help. Mixed methods may enhance RL agent decision-making. 

RL-based test execution planning improves coverage, real-world testing, and development. RL agents may use past data to rate trials by issue probability. Coding changes terminate testing. Resource and source code may affect RL test runtime. Testing works with more pipelines.
These findings are intriguing, but reinforcement learning for dynamic test execution in CI pipelines needs further investigation. Complex huge software system tests need RL development. Reward functions that balance testing speed, coverage, and fault detection require research. RL agent and testing infrastructure must operate together, making CI/CD tool and framework integration difficult.