Detecting and Characterizing Bots that Commit Code
Detecting and Characterizing Bots that Commit Code
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检测和表征提交代码的机器人
DOI:
10.1145/3379597.3387478
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发表时间:
2020
期刊:
影响因子:
--
通讯作者:
Mockus, Audris
中科院分区:
文献类型:
--
作者:
Dey, Tapajit;Mousavi, Sara;Ponce, Eduardo;Fry, Tanner;Vasilescu, Bogdan;Filippova, Anna;Mockus, Audris
BackgroundSome developer activity traditionally performed manually, such as making code commits, opening, managing, or closing issues is increasingly subject to automation in many OSS projects. Specifically, such activity is often performed by tools that react to events or run at specific times. We refer to such automation tools as bots and, in many software mining scenarios related to developer productivity or code quality, it is desirable to identify bots in order to separate their actions from actions of individuals.AimFind an automated way of identifying bots and code committed by these bots, and to characterize the types of bots based on their activity patterns.Method and ResultWe propose BIMAN, a systematic approach to detect bots using author names, commit messages, files modified by the commit, and projects associated with the commits. For our test data, the value for AUC-ROC was 0.9. We also characterized these bots based on the time patterns of their code commits and the types of files modified, and found that they primarily work with documentation files and web pages, and these files are most prevalent in HTML and JavaScript ecosystems. We have compiled a shareable dataset containing detailed information about 461 bots we found (all of which have more than 1000 commits) and 13,762,430 commits they created.
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DOI:
10.1109/botse.2019.00010
发表时间:
2019
期刊:
2019 IEEE/ACM 1st International Workshop on Bots in Software Engineering (BotSE)
影响因子:
--
作者:
Monperrus Martin
通讯作者:
Monperrus Martin
DOI:
--
发表时间:
2007
期刊:
Very Large Data Bases Conference
影响因子:
--
作者:
S. Helmer
通讯作者:
S. Helmer
DOI:
--
发表时间:
2016
期刊:
Interactions
影响因子:
--
作者:
Umer Farooq;J. Grudin
通讯作者:
J. Grudin
DOI:
--
发表时间:
2008
期刊:
SGAI Conferences
影响因子:
--
作者:
Alice Kerly;Richard Ellis;S. Bull
通讯作者:
S. Bull
DOI:
--
发表时间:
2018
期刊:
影响因子:
--
作者:
Carlene Lebeuf
通讯作者:
Carlene Lebeuf