Abstract
The paper presents a description of the developed approach and service for analyzing source code in Python. The service reduces the time for code review due to partial automation. The FastText algorithm is used to obtain vector representations of source code texts. A pre-trained neural network language model based on the transformer architecture was used to derive a possible natural language function assignment. A classifier based on the gradient boosting algorithm was used to detect duplicate PR. The developed service checks the changeset and publishes error and duplicate reports in changeset comment format after the changeset is published to a remote Git repository. The conducted testing did not reveal any errors that affect the operation. All the main functions of the system are performed correctly.
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Acknowledgements
This study was supported by the RFBR (project No. 20-07-00672) and by Ministry of Education and Science of Russia in framework of project № 075-00233-20-05 from 03.11.2020 «Research of intelligent predictive multimodal analysis of big data, and the extraction of knowledge from different sources».
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Moshkin, V., Andreev, I. (2023). Development of a Program Code Review System Using Machine Learning Methods. In: Dolinina, O., et al. Artificial Intelligence in Models, Methods and Applications. AIES 2022. Studies in Systems, Decision and Control, vol 457. Springer, Cham. https://doi.org/10.1007/978-3-031-22938-1_5
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