Abstract
Pull-Request (PR) is the primary method for developers to contribute code in GitHub. Code review can effectively ensure the quality of the code to be merged. The time of code review will affect the development progress of the entire project. Some researchers predict the duration of the review based on the initial attributes of PR as input attributes for building their prediction model. However, these methods ignore the temporal nature of these activities. In this paper, we propose a new method that uses the hidden Markov model (HMM) and the time series of developer activities. By considering the chronological sequence of developer activities, critical activities in PR are extracted to form a key activity sequence, by which HMM is used. To classify sequences, we collected the historical data of 5 projects from GitHub and conducted experiments. The results show that this method can effectively identify and predict PR’s duration to be reviewed at an early stage.
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Zhang, W., Pan, Z., Wang, Z. (2020). Prediction Method of Code Review Time Based on Hidden Markov Model. In: Wang, G., Lin, X., Hendler, J., Song, W., Xu, Z., Liu, G. (eds) Web Information Systems and Applications. WISA 2020. Lecture Notes in Computer Science(), vol 12432. Springer, Cham. https://doi.org/10.1007/978-3-030-60029-7_15
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DOI: https://doi.org/10.1007/978-3-030-60029-7_15
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