Commit Artifact Preserving Build Prediction
This program is tentative and subject to change.
In Continuous Integration (CI), accurate build prediction is crucial for minimizing development costs and enhancing efficiency. However, existing build prediction methods, typically based on predefined rules or machine learning classifiers employing feature engineering, have been constrained by their limited ability to fully capture the intricate details of commit artifacts, such as code change and commit messages. These artifacts are critical for understanding the commit under a build but have been inadequately utilized in existing approaches. To address this problem, we propose GitSense, a Transformer-based model specifically designed to incorporate the rich and complex information contained within commit artifacts for the first. GitSense employs an advanced textual encoder with built-in sliding window text samplers for textual features and a statistical feature encoder for extracted statistical features. This innovative approach allows for a comprehensive analysis of lengthy and intricate commit artifacts, surpassing the capabilities of traditional methods. We conduct comprehensive experiments to compare GitSense with three state-of-the-art models (SmartBuildSkip, BuildFast, HYBRIDCISAVE) and ChatGPT. The results are impressive, showing that GitSense outperforms these models in predicting failed builds, evidenced by 32.7%-239.0% better on F1-score, 23.9%-359.5% better on Precision, and 40.2%-169.5% better on Recall.
This program is tentative and subject to change.
Fri 20 SepDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
15:30 - 16:30 | |||
15:30 20mTalk | Enhancing Multi-Agent System Testing with Diversity-Guided Exploration and Adaptive Critical State Exploitation Technical Papers Ma Xuyan University of Chinese Academy of Sciences, Beijing, China; Laboratory for Internet Software Technologies, Institute of Software Chinese Academy of Sciences, Beijing, China;, Yawen Wang Institute of Software, Chinese Academy of Sciences, Junjie Wang Institute of Software, Chinese Academy of Sciences, Xiaofei Xie Singapore Management University, Boyu Wu University of Chinese Academy of Sciences, Beijing, China, Shoubin Li Institute of Software at Chinese Academy of Sciences; University of Chinese Academy of Sciences, Fanjiang Xu Institute of Software, Chinese Academy of Sciences, Qing Wang Institute of Software, Chinese Academy of Sciences | ||
15:50 20mTalk | Commit Artifact Preserving Build Prediction Technical Papers Guoqing Wang Peking University, Zeyu Sun Institute of Software, Chinese Academy of Sciences, Yizhou Chen Peking University, Yifan Zhao Peking University, Qingyuan Liang Peking University, Dan Hao Peking University | ||
16:10 20mTalk | Detecting Build Dependency Errors in Incremental Builds Technical Papers Jun Lyu Nanjing University, Shanshan Li Software Institute, Nanjing University, He Zhang Nanjing University, Yang Zhang Nanjing University, Guoping Rong Nanjing University, Manuel Rigger National University of Singapore DOI |