I-Corps: Translation Potential of Optimizing Regression Testing in Software Development

I-Corps：软件开发中优化回归测试的转化潜力

• 批准号: 2405355
• 负责人: Milos Gligoric
• 金额: $ 5万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-02-15 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2405355&HistoricalAwards=false
• 关键词: Corps; Translation; Potential; Optimizing; Regression

The broader impact of this I-Corps project is the development of technology that will enable companies to substantially reduce the number of software tests they have to run for any code change, thus reducing their software testing cost and improving the developer’s productivity. This technology has the potential to reduce testing time by over 50% on average, across a large number of code changes. The integration of the technology into already existing workflows will introduce minimal disruptions and remain hidden from most software developers, increasing the chance for adoption. This research will bring: (a) a reduction in testing time and faster feedback to developers, (b) a reduction in required resources to run tests which will reduce overall cost and maintenance cost, and (c) an alignment with green computing efforts and carbon neutrality.This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of a regression test selection tool (RTS) that reduces testing cost by skipping tests that are not impacted by code changes. This technology has the potential to be integrated in most common development workflows. The tool can capture dependencies for each test (or a group of similar tests) on fine-grained code level (e.g., classes, methods, and functions). Once a user changes one of the code elements, the test tool will automatically identify a set of tests to run, and those to skip as they are not impacted by changes. The tool is triggered by specific events and needs to be able to access various storage services to retrieve and store test dependencies. Early evaluations of the tool have shown a reduction of the testing budget by over 50%.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.

该I-Corps项目的更广泛的影响是技术的开发，该技术将使公司能够大大减少为任何代码更改所需的软件测试数量，从而降低其软件测试成本并提高开发人员的生产率。在大量代码更改中，该技术有可能平均将测试时间缩短超过50％。将技术集成到已经存在的工作流程中将引入最小的中断，并隐藏在大多数软件开发人员中，从而增加了采用的机会。 This research will bring: (a) a reduction in testing time and faster feedback to developers, (b) a reduction in required resources to run tests which will reduce overall cost and maintenance cost, and (c) an alignment with green computing efforts and carbon neutrality.This I-Corps project utilizes expert learning coupled with a first-hand investment of the industry ecosystem to assess the translation potential of the technology.该解决方案基于回归测试选择工具（RTS）的开发，该工具通过跳过不受代码更改影响的测试来降低测试成本。该技术有可能集成到大多数常见的开发工作流程中。该工具可以在细粒度的代码级别（例如类，方法和功能）上捕获每个测试（或一组类似测试）的依赖项。一旦用户更改了代码元素之一，测试工具将自动识别一组要运行的测试，而这些测试不会受到更改影响。该工具是由特定事件触发的，并且需要能够访问各种存储服务以检索和存储测试依赖项。对该工具的早期评估显示，将测试预算减少了50％以上。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响审查标准来评估，被视为珍贵的支持。