一句话：提出基于 NSGA-II 的 CI 构建失败检测工具，通过多目标进化搜索生成预测规则，在 56,019 个 CI 构建上超越现有方法。
持续集成（CI）通过自动化构建帮助开发者快速集成代码变更，但 CI 构建失败已成为阻碍开发推进的主要障碍。本文提出 BF-Detector，一个自动化的 CI 构建失败检测工具。该工具适配非支配排序遗传算法（NSGA-II），围绕两个相互冲突的目标函数进行搜索，以生成最优预测规则，从而同时应对少数类（失败构建）与多数类（通过构建）的不平衡问题。作者在包含 56,019 个 CI 构建的基准数据集上评估了工具的有效性，结果表明该技术优于当前最先进的方法，能够在失败构建与通过构建之间取得更好的平衡。BF-Detector 已公开可用，并附带演示视频。
Continuous Integration (CI) aims at supporting developers in inte-grating code changes quickly through automated building. How-ever, there is a consensus that CI build failure is a major barrierthat developers face, which prevents them from proceeding furtherwith development. In this paper, we introduceBF-Detector, anautomated tool to detect CI build failure. Based on the adaptationof Non-dominated Sorting Genetic Algorithm (NSGA-II), our toolaims at finding the best prediction rules based on two conflictingobjective functions to deal with both minority and majority classes.We evaluated the effectiveness of our tool on a benchmark of 56,019CI builds. The results reveal that our technique outperforms state-of-the-art approaches by providing a better balance between bothfailed and passed builds.BF-Detectortool is publicly available,with a demo video, at: https://github.com/stilab-ets/BF-Detector.
DOI 原文 ·
@inproceedings{paperbot6454,
title = {BF-detector: an automated tool for CI build failure detection},
author = {Islem Saidani and Ali Ouni and Moataz Chouchen and Mohamed Wiem Mkaouer},
booktitle = {FSE},
year = {2021},
doi = {10.1145/3468264.3473115}
}