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

由于完全测试软件的困难性，人工代码评审仍然是软件保证的重要贡献者。 当前的代码审查工具提供了相对简单的信息，例如当前源代码和以前版本之间的差异列表。 因此，许多代码审查忽略了差异的重要含义，例如不充分的测试或软件错误。 这项研究正在调查两个软件版本之间差异的更丰富的表现形式，目的是更容易发现缺陷，并将它们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的“大数据”方法的典范。具体而言，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摘要。 在代码评审的上下文中，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行为的影响。 由于属性摘要可能非常庞大，因此它们将作为当前软件版本和上一个软件版本的属性之间的差异来呈现。 这项研究还解决了实际应用这一方法的挑战。 新的技术正在开发，以提高测试套件的充分性，有效地获取痕迹，计算不变量。 该方法的核心是，软件的版本到版本的更改通常是增量的和包含的，允许大量重用以前运行的数据。 在解决这些挑战的过程中，研究正在开发和扩展一个可扩展的自动代码审查基础设施，使实验室实验和案例研究的方法的实际验证。 最终，这项研究有望将新的工具交到实践软件开发人员手中，帮助他们找到bug并改进他们的测试套件。

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.