Will They Like This? Evaluating Code Contributions with Language Models

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Bugram: Bug detection with n-gram language models (2016, methodology): “Statistical language models have been successfully used for tasks including code completion [37,51], fault localization and coding style consistency checking [2, 14].”
PyDriller: Python framework for mining software repositories (2018, background): “3264598 can empirically investigate, understand, and uncover useful and actionable insights for software engineering, such as understanding the impact of code smells [13–15], exploring how developers are doing code reviews [2, 4, 10, 21] and which testing practices they follow [20], predicting class”
Products, developers, and milestones: how should I build my N-Gram language model (2015, background): “Hellendoorn showed that language models can be used to determine how well a code contribution to a project matches the project’s coding style and thus can indicate if a contribution will be rejected and need further work [12].”
Are deep neural networks the best choice for modeling source code? (2017, methodology): “Recent bug-detection work has built models on snapshots at one month apart and tested on the ensuing changes until the next snapshot [31] in order tomake the computation tractable; the models in this work can be updated with every commit from a project’s inception and run in the order of minutes acr”