SHF: Small: Scalable and Practical Detection of Invariants for Software Inspection

SHF：小型：可扩展且实用的软件检查不变量检测

• 批准号: 1719155
• 负责人: William Griswold
• 金额: $ 50万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1719155&HistoricalAwards=false
• 关键词: SHF; Small; Scalable; Practical; Detection

Due to the intractability of completely testing software, code review by humans remains an important contributor to software assurance.  Current tools for code review provide relatively simple information, such as a listing of the differences between the current source code and the previous version.  Consequently, many code reviews miss important implications of the differences, such as inadequate testing or a software bug.  This research is investigating richer representations of the difference between two software versions, with the goal of making it easier to spot defects and keep them out of software slated for release.  The approach hinges on using runtime analysis for mining software repositories, making it an exemplar of a "big data" approach to quantitative software engineering.Specifically, the research is exploring the promise of a foundational technology called invariant detection, which gathers data from the software's runs and produces a summary of the behavioral properties of its components.  In the context of code review, these properties, if they disagree with expectations, reveal inadequate testing or actual software defects.  Because the properties are not just syntactic, they can highlight the impact of code changes on the behavior of unmodified code.  Because property summaries can be voluminous, they are presented as a difference between the properties of the current software version and the previous one.  The research is also addressing challenges to practical application of the approach.  Novel techniques are being developed for improving test suite adequacy, efficiently acquiring traces, and calculating invariants.  Core to the approach is that the version-to-version changes to software are often incremental and contained, permitting substantial reuse of data from prior runs.  In the course of addressing these challenges, the research is developing and extending a scalable, automated code review infrastructure, enabling practical validation of the approach in both laboratory experiments and case studies.  Ultimately, the research holds the promise to put new tools in the hands of practicing software developers, helping them find bugs and improve their test suites.

由于完全测试软件的棘手性，人类的代码审查仍然是软件保证的重要原因。当前用于代码审核的工具提供了相对简单的信息，例如列出当前源代码和上一个版本之间的差异。因此，许多代码评论错过了差异的重要含义，例如测试不足或软件错误。这项研究正在调查两个软件版本之间差异的富裕表示，目的是使发现缺陷变得更容易，并使它们无法释放。该方法取决于使用运行时分析作为采矿软件存储库，使其成为定量软件工程的“大数据”方法的典范。特别是，该研究正在探索称为不变性检测的基础技术的希望，该技术收集了该软件运行中的数据，并产生了其组成属性的概述。在代码审查的背景下，如果这些属性不同意期望，则揭示了测试不足或实际软件缺陷。由于属性不仅是句法，因此它们可以强调代码更改对未修改代码行为的影响。由于属性摘要可能是大量的，因此将它们作为当前软件版本的属性与上一个属性之间的区别表示。这项研究还针对实用新技术的挑战正在开发，以改善测试套件的充分性，有效地获得痕迹和计算不变性。该方法的核心是，对软件的版本之间的更改通常是增量并包含的，从而允许从先前运行中大量重复使用数据。在解决这些挑战的过程中，该研究正在开发和扩展可扩展的，自动的代码审查基础架构，从而在实验室实验和案例研究中对方法进行实际验证。最终，该研究有望将新工具掌握在练习软件开发人员的手中，帮助他们找到错误并改善测试套件。

项目成果

ANEL: Robust Mobile Network Programming Using a Declarative Language

ANEL：使用声明性语言进行稳健的移动网络编程

• DOI: 10.1145/3197231.3197237
• 发表时间: 2018
• 期刊: 2018 IEEE/ACM 5th International Conference on Mobile Software Engineering and Systems (MOBILESoft
• 作者: Jin, Xinxin;Griswold, William G.;Zhou, Yuanyuan
• 通讯作者: Zhou, Yuanyuan

Semantics-assisted code review: An efficient tool chain and a user study

语义辅助代码审查：高效的工具链和用户研究

• DOI: 10.1109/ase.2017.8115666
• 发表时间: 2017
• 期刊: 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE
• 作者: Menarini, Massimiliano;Yan, Yan;Griswold, William G.
• 通讯作者: Griswold, William G.