CAREER: Maximal and Scalable Unified Debugging for the JVM Ecosystem

职业：JVM 生态系统的最大且可扩展的统一调试

• 批准号: 2131943
• 负责人: Lingming Zhang
• 金额: $ 51.98万
• 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: 2021
• 资助国家: 美国
• 起止时间: 2021-07-15 至 2026-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2131943&HistoricalAwards=false
• 关键词: CAREER; Maximal; Scalable; Unified; Debugging

The software industry all over the world has contributed to the massive culture of support around Java, one of the most popular programming languages. The Java runtime, or Java Virtual Machine (JVM), has become a software ecosystem on its own. Nowadays, hundreds of popular JVM languages (including Kotlin, Scala, and Groovy) have been developed/adopted under different platforms (including Oracle JDK and Android SDK), build systems (including Gradle and Maven), and JVM implementations (including HotSpot and OpenJ9). For example, Google just promoted Kotlin to the No.1 preferred language for Android development at Google I/O 2019. The huge and heterogeneous ecosystem of JVM raises unique challenges to automated debugging, including both fault localization and repair. This project proposes to re-think the role of a foundational concept of program mutation, that is, systematic program transformation, in automated debugging. Program mutation has been widely adopted in traditional mutation testing and program repair, and the investigator conjectures, based on preliminary work, that it can be used to transform and advance the state-of-the-art in automated debugging for software written with technologies from the entire JVM ecosystem and beyond. Specifically, the project focuses on the following research thrusts: (1) unifying both fault localization and repair via program mutation to boost each other, (2) automatically inferring up-to-date advanced mutators from big code corpora for maximal unified debugging, since existing program mutators are often limited and may easily become obsolete, (3) developing novel techniques to optimize patch executions for scalable unified debugging, since patch execution can be extremely time-consuming, and (4) supporting unified debugging of the entire heterogeneous JVM ecosystem. The project will unify program mutations across various dimensions for the first time, e.g., across JVM languages and platforms, across code types (including source, test, and build code), and even across JVM boundaries. Ultimately, the project aims for a practical debugging system to benefit JVM ecosystem developers all over the world. The overarching idea of unified debugging can also substantially impact the ways that both researchers and practitioners view, design, and apply automated debugging -- fault localization always requires manual repair while program repair only works for some bugs; in contrast, unified debugging can support the most automated debugging possible for each bug, and broaden the effective range of the entire program repair area to all possible bugs. The project will integrate the research results into SE curriculum, K-12 camps, software testing contests, and industrial collaborations.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.

世界各地的软件行业为Java围绕最受欢迎的编程语言之一的Java提供了巨大的支持文化。 Java运行时或Java Virtual Machine（JVM）已自行成为软件生态系统。如今，在不同平台（包括Oracle JDK和Android SDK），构建系统（包括Gradle and Maven）以及JVM实现（包括Hotspot和OpenJ9）下开发/采用了数百种流行的JVM语言（包括Kotlin，Scala和Groovy）。例如，Google刚刚将Kotlin推广到Google I/O 2019上的Android开发的第一名首选语言。JVM的巨大且异构的生态系统对自动调试提出了独特的挑战，包括故障定位和维修。该项目建议重新考虑程序突变基础概念的作用，即系统的程序转换，在自动调试中。程序突变已在传统的突变测试和程序维修中广泛采用，研究者的猜想是基于初步工作的，它可以用来转换和推进最新的自动调试中最先进的调试，用于使用来自整个JVM生态系统及以后的技术编写的软件。 Specifically, the project focuses on the following research thrusts: (1) unifying both fault localization and repair via program mutation to boost each other, (2) automatically inferring up-to-date advanced mutators from big code corpora for maximal unified debugging, since existing program mutators are often limited and may easily become obsolete, (3) developing novel techniques to optimize patch executions for scalable unified debugging, since patch execution can be extremely耗时，（4）支持整个异质JVM生态系统的统一调试。该项目将首次统一各个维度的程序突变，例如，跨JVM语言和平台，跨代码类型（包括源，测试和构建代码），甚至在JVM边界之间进行统一。最终，该项目旨在实施实用的调试系统，以使世界各地的JVM生态系统开发人员受益。统一调试的总体思想还可以实质性地影响研究人员和从业人员使用自动调试的方式 - 故障本地化始终需要手动维修，而程序维修仅适用于某些错误；相比之下，统一的调试可以支持每个错误最大的调试，并将整个程序维修区域的有效范围扩大到所有可能的错误。该项目将将研究结果纳入SE课程，K-12营地，软件测试竞赛和工业合作。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和更广泛影响的评估评估标准的评估值得支持的。

项目成果

Test-Case Prioritization for Configuration Testing

• DOI: 10.1145/3460319.3464810
• 发表时间: 2021-01-01
• 期刊: ISSTA '21: PROCEEDINGS OF THE 30TH ACM SIGSOFT INTERNATIONAL SYMPOSIUM ON SOFTWARE TESTING AND ANALYSIS
• 作者: Cheng, Runxiang;Zhang, Lingming;Xu, Tianyin
• 通讯作者: Xu, Tianyin

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

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

Evaluating and Improving Hybrid Fuzzing

• DOI: 10.1109/icse48619.2023.00045
• 发表时间: 2023-05
• 期刊: 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE)
• 作者: Ling Jiang;Hengchen Yuan;Mingyuan Wu;Lingming Zhang;Yuqun Zhang

Less training, more repairing please: revisiting automated program repair via zero-shot learning

• DOI: 10.1145/3540250.3549101
• 发表时间: 2022-07
• 期刊: Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering
• 作者: Chun Xia;Lingming Zhang