PACE: A Program Analysis Framework for Continuous Performance Prediction
Chidera Biringa Gokhan Kul
Software development teams establish elaborate continuous integration
pipelines containing automated test cases to accelerate the development process
of software. Automated tests help to verify the correctness of code
modifications decreasing the response time to changing requirements. However,
when the software teams do not track the performance impact of pending
modifications, they may need to spend considerable time refactoring existing
code. This paper presents PACE, a program analysis framework that provides
continuous feedback on the performance impact of pending code updates. We
design performance microbenchmarks by mapping the execution time of functional
test cases given a code update. We map microbenchmarks to code stylometry
features and feed them to predictors for performance predictions. Our
experiments achieved significant performance in predicting code performance,
outperforming current state-of-the-art by 75% on neural-represented code
stylometry features.