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

这个i-Corps项目的更广泛影响是技术的开发，该技术将使公司能够大幅减少对任何代码更改必须运行的软件测试数量，从而降低软件测试成本并提高开发人员的生产率。在进行大量代码更改时，这项技术有可能将测试时间平均减少50%以上。将该技术集成到现有的工作流程中将带来最小的中断，并对大多数软件开发人员隐藏起来，从而增加采用的机会。这项研究将带来：(A)减少测试时间并更快地向开发人员反馈，(B)减少运行测试所需的资源，从而降低总体成本和维护成本，以及(C)与绿色计算努力和碳中和保持一致。这个i-Corps项目利用经验学习和对行业生态系统的第一手调查来评估该技术的翻译潜力。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。这项技术有可能集成到最常见的开发工作流中。该工具可以捕获细粒度代码级别(例如，类、方法和函数)上的每个测试(或一组类似测试)的依赖关系。一旦用户更改了其中一个代码元素，测试工具将自动识别一组要运行的测试，以及要跳过的测试，因为它们不受更改的影响。该工具由特定事件触发，需要能够访问各种存储服务以检索和存储测试依赖项。该工具的早期评估显示，测试预算减少了50%以上。这一奖励反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.