SHF: Medium: Collaborative Research: Enhancing Continuous Integration Testing for the Open-Source Ecosystem

SHF：媒介：协作研究：加强开源生态系统的持续集成测试

• 批准号: 2141474
• 负责人: Lingming Zhang
• 金额: $ 36.3万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2023-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2141474&HistoricalAwards=false
• 关键词: SHF; Medium; Collaborative; Research; Enhancing

Continuous integration (CI) is an important software development activity that aims to improve software development by automating software compilation and regression testing. Recent studies report that CI helps developers deploy faster and reduce development cost. Given these success stories, CI has attracted rapidly increasing interest and adoption, e.g., Travis CI, the currently most popular CI service, is used by over 300,000 GitHub projects. Despite the success of CI, developers report they would like to see improvements in CI. First, they want to faster obtain regression test results. Second, they want better handling of so-called flaky tests, which are regression tests that can non-deterministically pass or fail, and whose failures negatively affect developer's productivity. Third, developers report that CI builds do not provide sufficient debugging assistance for reasoning about failed regression tests. While regression testing has been studied for over three decades, it has not been studied in the context of CI until recently.To substantially improve regression testing in CI, the PIs propose to develop novel techniques and tools that address three important challenges: (1) test selection to speed up regression testing and the development cycle, (2) test reliability to mitigate the problems that flaky tests introduce, and (3) debugging assistance to ease the effort of diagnosing and fixing the true and flaky regression test failures. The PIs plan to develop techniques and tools based on a mix of static and dynamic program analyses, leveraging not only information from two project revisions (as traditional in regression testing) but also from all historical build and testing information available in CI testing. The PIs plan to embody their techniques in a tool-set and evaluate them extensively on open-source projects and in industrial collaborations. The broader impacts of enhancing continuous integration testing are to allow software developers to faster build higher quality software, which can benefit our modern society that greatly depends on software.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

连续集成（CI）是一项重要的软件开发活动，旨在通过自动化软件编译和回归测试来改善软件开发。 最近的研究报告说，CI可帮助开发人员更快地部署并降低开发成本。鉴于这些成功案例，CI吸引了迅速增加的兴趣和采用，例如，当前最受欢迎的CI服务Travis CI已被300,000多个GitHub项目使用。 尽管CI取得了成功，但开发人员报告说，他们希望看到CI的改善。首先，他们想更快地获得回归测试结果。 其次，他们希望更好地处理所谓的片状测试，这些测试是可以非确定性通过或失败的回归测试，并且失败会对开发人员的生产力产生负面影响。 第三，开发人员报告说，CI构建没有为有关回归测试失败的推理提供足够的调试帮助。 While regression testing has been studied for over three decades, it has not been studied in the context of CI until recently.To substantially improve regression testing in CI, the PIs propose to develop novel techniques and tools that address three important challenges: (1) test selection to speed up regression testing and the development cycle, (2) test reliability to mitigate the problems that flaky tests introduce, and (3) debugging assistance to ease the effort of diagnosing and修复真实且片状的回归测试失败。 PIS计划根据静态和动态程序分析的组合开发技术和工具，不仅利用了两个项目修订（作为回归测试中的传统）信息，还利用CI测试中所有可用的历史构建和测试信息。 PIS计划在工具集中体现其技术，并在开源项目和工业合作中广泛评估它们。增强连续集成测试的更广泛的影响是使软件开发人员能够更快地构建更高质量的软件，这可以使我们的现代社会受到极大的依赖的软件的好处。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估标准来评估的。

项目成果

ITfuzz: Coverage-guided Fuzzing for JVM Just-in-Time Compilers

ITfuzz：针对 JVM 即时编译器的覆盖引导模糊测试

• 发表时间: 2023
• 期刊: Proceedings of the IEEE/ACM International Conference on Software Engineering
• 作者: Wu, Mingyuan;Lu, Minghai;Cui, Heming;Chen, Junjie;Zhang, Yuqun;Zhang, Lingming
• 通讯作者: Zhang, Lingming

An extensive study on pre-trained models for program understanding and generation

• DOI: 10.1145/3533767.3534390
• 发表时间: 2022-07
• 期刊: Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis
• 作者: Zhengran Zeng;Hanzhuo Tan;Haotian Zhang;Jing Li;Yuqun Zhang;Lingming Zhang

Automated Program Repair in the Era of Large Pre-trained Language Models

• DOI: 10.1109/icse48619.2023.00129
• 发表时间: 2023-05
• 期刊: 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE)
• 作者: Chun Xia;Yuxiang Wei-;Lingming Zhang

MirrorTaint: Practical Non-intrusive Dynamic Taint Tracking for JVM-based Microservice Systems

MirrorTaint：基于 JVM 的微服务系统的实用非侵入式动态污点跟踪

• 发表时间: 2023
• 期刊: Proceedings of the IEEE/ACM International Conference on Software Engineering
• 作者: Ouyang, Yicheng;Shao, Kailai;Chen, Kunqiu;Shen, Ruobing;Chen, Chao;Xu, Mingze;Zhang, Yuqun;Zhang, Lingming
• 通讯作者: Zhang, Lingming

Coverage-guided tensor compiler fuzzing with joint IR-pass mutation

• DOI: 10.1145/3527317
• 发表时间: 2022-02
• 期刊: Proceedings of the ACM on Programming Languages
• 作者: Jiawei Liu;Yuxiang Wei-;Sen Yang;Yinlin Deng;Lingming Zhang