Detecting performance patterns with deep learning

Performance has a major impact on the overall quality of software projects. Performance bugs---bugs that substantially decrease run-time---have long been studied in software engineering, and yet they remain incredibly difficult for developers to handle. Because these bugs do not cause fail-stop errors, they are both harder to discover and to fix. As a result, techniques to help programmers detect and reason about performance are needed for managing performance bugs. Here we propose a static, probabilistic embedding technique to provide developers with useful information about potential performance bugs at the statement level. Using Leetcode samples scraped from real algorithms challenges, we use DeepWalk to embed data dependency graphs in Euclidean space. We then describe how these graph embeddings can be used to detect which statements in code are likely to contribute to performance bugs.