CAREER: Data-Driven Debugging of Complex Program Changes

职业：复杂程序更改的数据驱动调试

• 批准号: 1845446
• 负责人: Na Meng
• 金额: $ 50万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1845446&HistoricalAwards=false
• 关键词: CAREER; Data; Driven; Debugging; Complex

After the initial release of a software application, developers continue changing it by adding new features, fixing software defects, or improving the implementation of existing features. As the application becomes older and larger, program changes become more complex. It becomes more difficult for developers to change or maintain the software efficiently and correctly. Slowly and incorrectly evolved software systems negatively impact both software producers and consumers; these systems can cause tremendous economic costs, put customers' data and privacy at risk, and even threaten people's lives. This project addresses this problem by helping computers and developers better understand, check, and apply complex changes. The project's novelty is in new methods and tools to characterize, model, validate, and suggest code modifications. The project's impacts are increasing programmer productivity, improving software reliability, reducing software cost, and protecting people and privacy data from issues caused by problematic software upgrades. The project will conduct empirical studies of software projects (in the GitHub repository) to understand and characterize multi-change edits "in the wild" to discover frequently co-changed patterns among code and non-code entities. Using these inferred patterns, the project will classify multi-change edits into different types such as bug fixes, refactorings, and feature enhancements. Techniques will be devised to make multi-edit changes, to ensure their correctness, and to automatically repair programs' multi-change edit sequences. These capabilities will be combined with delta debugging and dynamic patch validation in a a prototype editing and testing tool called Oedit, which will automatically recover programs from erroneous software updates and upgrades. The long-term vision is to provide a programming environment that supports complex, multi-change edits, with attention to their correctness, and with self-healing capabilities.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.

在软件应用程序的初始发布之后，开发人员通过添加新功能、修复软件缺陷或改进现有功能的实现来继续对其进行更改。随着应用程序变得更老、更大，程序更改变得更加复杂。对于开发人员来说，有效和正确地更改或维护软件变得更加困难。缓慢且不正确的软件系统会对软件生产商和消费者产生负面影响;这些系统会造成巨大的经济成本，使客户的数据和隐私处于危险之中，甚至威胁到人们的生命。这个项目通过帮助计算机和开发人员更好地理解、检查和应用复杂的更改来解决这个问题。该项目的新奇在于新的方法和工具来描述、建模、验证和建议代码修改。该项目的影响是提高程序员的生产力，提高软件可靠性，降低软件成本，并保护人员和隐私数据免受有问题的软件升级所引起的问题。该项目将对软件项目（在GitHub存储库中）进行实证研究，以了解和表征“野外”的多更改编辑，以发现代码和非代码实体之间频繁共同更改的模式。使用这些推断出的模式，该项目将把多更改编辑分为不同的类型，如错误修复、重构和功能增强。将设计技术来进行多编辑更改，以确保其正确性，并自动修复程序的多更改编辑序列。这些功能将与增量调试和动态补丁验证相结合，在一个名为Oedit的原型编辑和测试工具中，该工具将自动从错误的软件更新和升级中恢复程序。长期愿景是提供一个支持复杂、多更改编辑的编程环境，关注其正确性，并具有自我修复能力。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Investigating The Reproducibility of NPM Packages

• DOI: 10.1109/icsme46990.2020.00071
• 发表时间: 2020-05
• 期刊: 2020 IEEE International Conference on Software Maintenance and Evolution (ICSME)
• 作者: Pronnoy Goswami;Saksham Gupta;Zhiyuan Li;Na Meng;Daphne Yao

An Empirical Study on API Parameter Rules

• DOI: 10.1145/3377811.3380922
• 发表时间: 2020-06
• 期刊: 2020 IEEE/ACM 42nd International Conference on Software Engineering (ICSE)
• 作者: Hao Zhong;Na Meng;Zexuan Li;Li Jia

Example-Based Vulnerability Detection and Repair in Java Code

• DOI: 10.1145/3524610.3527895
• 发表时间: 2022-03
• 期刊: 2022 IEEE/ACM 30th International Conference on Program Comprehension (ICPC)
• 作者: Y. Zhang;Ya Xiao;Md Mahir Asef Kabir;D. Yao;Na Meng

Detecting Build Conflicts in Software Merge for Java Programs via Static Analysis

• DOI: 10.1145/3551349.3556950
• 发表时间: 2022-10
• 期刊: Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering
• 作者: Sheikh Shadab Towqir;Bowen Shen;Muhammad Ali Gulzar;Na Meng

Inferring and Applying Def-Use Like Configuration Couplings in Deployment Descriptors

• DOI: 10.1145/3324884.3416577
• 发表时间: 2020-09
• 期刊: 2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE)
• 作者: Chengyuan Wen;Yaxuan Zhang;Xiao He;Na Meng