SHF: Small: Science and Tools for Intelligent Developer Testing

软件可靠性在美国企业、政府和社会中扮演着至关重要的角色。尽管在软件验证和确认方面取得了很大进展，但软件测试仍然是迄今为止使用最广泛的提高软件可靠性的技术。在各种类型的测试中，开发人员测试被广泛认为是提高软件可靠性的重要和有价值的手段。开发人员测试的受欢迎程度和好处已经在行业中得到了很好的证明；然而，众所周知，手动开发人员测试是劳动密集型的，而且往往不足以全面行使被测软件的行为来暴露其隐藏的错误。为了解决这个问题，常见的方法之一是使用测试基础设施和工具来减少或补充手动测试工作，以实现更高的软件可靠性。在过去的十年中，软件测试研究界在自动测试生成方面取得了重大进展。随着研究界最近的各种科学进步，一个问题自然出现了：在接下来的十年里，开发人员测试领域将实现什么大胆的目标，为开发人员带来更高的测试有效性和效率？为了解决这个问题，这个项目研究了智能开发人员测试的科学和工具，从根本上提高了对智能开发人员测试的基础、技术和工具的知识和理解。该项目通过在部署的软件中显示软件开发过程中的更多错误来提高软件可靠性。该项目专注于从两个主要方式(自然语言接口和持续学习)向开发人员测试工具灌输智能的科学和工具，作为实现智能软件工程愿景的努力的一部分。该项目开发了新颖实用的智能开发人员测试技术和工具，具有很高的影响行业潜力。特别是，该项目专注于参数化单元测试，这是对传统单元测试的改进，因为它们可以很容易地被自动化工具扩展，从而在重用开发人员编写的Oracle的同时增加代码覆盖率。PI计划开发智能工具，使开发人员编写参数化单元测试变得更加容易，目标是自动将开发人员的意图转换为可执行测试用例的一部分。这个项目通过探索一系列问题来推动科学的发展，例如，如何定义或确定开发人员测试中的智能水平，如何在开发人员测试工具中带来高水平的智能，如何协同集成机器智能和人类智能(例如，领域知识或洞察力)，以有效地处理开发人员测试中具有挑战性的任务。该项目涉及与行业合作伙伴的研究合作，并涉及来自代表性不足群体的学生的参与。该奖项反映了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。

Software dependability plays a critical role in United States businesses, government, and society. Although much progress has been made in software verification and validation, software testing remains by far the most widely used technique for improving software dependability. Among various types of testing, developer testing has been widely recognized as an important and valuable means of improving software dependability. The popularity and benefits of developer testing have been well witnessed in industry; however, manual developer testing is known to be labor intensive, and is often insufficient in comprehensively exercising behavior of the software under test to expose its hidden bugs. To address the issue, one of the common ways is to use testing infrastructures and tools to reduce or complement manual testing effort to achieve higher software dependability. In the past decade, the software testing research community has made significant progress in automatic test generation. With various recent scientific advances by the research community, a question naturally arises: what would be the audacious goal for the field of developer testing in the upcoming decade to bring a much higher testing effectiveness and efficiency to developers? To address this question, this project investigates the science and tools of intelligent developer testing, fundamentally advancing knowledge and understanding in foundations, techniques, and tools for intelligent developer testing. The project improves software dependability by revealing more bugs during software development before they manifest in deployed software.The project focuses on science and tools for instilling intelligence from two major ways (natural language interfacing and continuous learning) into developer testing tools as part of the efforts for realizing the vision of intelligent software engineering. The project develops novel and practical techniques and tools of intelligent developer testing that have high potential to impact the industry. In particular, the project focuses on parameterized unit tests, which are an improvement over conventional unit tests because they can easily be extended by automated tools to increase code coverage while reusing developer-written oracles. The PI plans to develop intelligent tools that will make it significantly easier for developers to write parameterized unit tests with the goal of automatically translating developer intents into parts of executable test cases. This project advances the science by exploring a series of questions, e.g., how to define or determine levels of intelligence in developer testing, how to bring high levels of intelligence in developer testing tools, how to synergistically integrate machine intelligence and human intelligence (e.g., domain knowledge or insight) to effectively tackle challenging tasks in developer testing. The project involves research collaborations with industrial partners and involves participation of students from underrepresented groups.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.