Identifying defective code (bugs) in commits from a software repository may improve productivity and reduce the amount of corrections that need to be applied in production. We present a framework based on deep learning that could help identify buggy commits. The algorithms are trained on a well-known cloud infrastructure management code repository
Key Takeaways
- Deep learning could be used for identifying buggy commits
- Accuracy of identification differs depending on the approach
- Open source dataset and example code are available to facilitate reproduction and building applications around the framework
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