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困难性，人类的代码审查仍然是软件保证的重要贡献者。  当前的代码审查工具提供相对简单的信息，例如当前源代码与先前版本之间的差异列表。  因此，许多代码审查都忽略了差异的重要含义，例如测试不充分或软件错误。  这项研究正在调查两个软件版本之间差异的更丰富的表示，目的是更容易地发现缺陷并将其排除在计划发布的软件之外。  该方法依赖于使用运行时分析来挖掘软件存储库，使其成为定量软件工程“大数据”方法的典范。具体来说，该研究正在探索一种称为不变检测的基础技术的前景，该技术从软件的运行中收集数据并生成其组件的行为属性的摘要。  在代码审查的背景下，如果这些属性与预期不一致，则表明测试不充分或实际的软件缺陷。  因为属性不仅仅是语法上的，它们可以突出代码更改对未修改代码的行为的影响。  由于属性摘要可能会很庞大，因此它们表示为当前软件版本与前一版本的属性之间的差异。  该研究还正在解决该方法实际应用中的挑战。  人们正在开发新的技术来提高测试套件的充分性、有效地获取痕迹和计算不变量。  该方法的核心是软件的版本间更改通常是增量且包含的，从而允许大量重用先前运行的数据。  在应对这些挑战的过程中，该研究正在开发和扩展可扩展的自动化代码审查基础设施，从而能够在实验室实验和案例研究中对该方法进行实际验证。  最终，这项研究有望将新工具交给实践中的软件开发人员，帮助他们发现错误并改进他们的测试套件。

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.