CAREER: Toward Effective, Predictable, and Consistent Software Testing

职业：实现有效、可预测和一致的软件测试

• 批准号: 1942055
• 负责人: Rene Just
• 金额: $ 55万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1942055&HistoricalAwards=false
• 关键词: CAREER; Toward; Effective; Predictable; Consistent; CAREER; Toward; Effective; Predictable; Consistent

Billions of people depend on software systems every day, but software remains buggy and vulnerable. Software testing is the predominant approach for software quality assurance (QA), and hundreds of thousands of developers are involved in QA activities on a daily basis. Developers heavily rely on intuition, experience, and familiarity with a code base when developing tests, and when reasoning about test effectiveness and consistency. However, experience varies widely, which has negative impacts on software quality and developer productivity. Addressing these challenges, this project develops practical end-to-end solutions for effective and consistent software testing that integrate well with existing QA practices. It aims to substantially increase the quality of the software that pervades modern technology, by providing developers with a framework and methodology for systematic software testing with quantifiable efficacy.The overall goal of this project is to advance software quality by making software testing more effective, predictable, and consistent. To achieve this goal, the proposed research co-develops a new mutation-based testing approach along with a methodology to quantify its efficacy. Specifically, this project (1) develops models for program context and test goal utility, (2) builds upon these models and develops a learning-based approach for context-sensitive predictions of test goal utility and ranking test goals, and (3) develops a probabilistic method that assesses how representative a set of test goals is for bugs encountered during software development. In addition to the research artifacts, this project will provide publicly available artifacts that enable training of a new generation of software engineers.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.

数十亿人每天都依靠软件系统，但是软件仍然是瓦解和脆弱的。软件测试是软件质量保证（QA）的主要方法，每天有成千上万的开发人员参与质量检查活动。开发人员在开发测试时以及关于测试有效性和一致性的推理时都严重依赖于直觉，经验和对代码基础的熟悉。但是，经验差异很大，这对软件质量和开发人员的生产率产生了负面影响。在应对这些挑战时，该项目开发了实用的端到端解决方案，以实现有效且一致的软件测试，并与现有的质量检查实践融为一体。它旨在通过为开发人员提供具有可量化功效的系统软件测试的框架和方法，从而大大提高遍及现代技术的质量。该项目的总体目标是通过使软件测试更有效，可预测和一致来提高软件质量。为了实现这一目标，拟议的研究共同开发了一种新的基于突变的测试方法以及量化其功效的方法。具体而言，该项目（1）为程序上下文和测试目标实用程序开发模型，（2）基于这些模型，并开发了一种基于学习的方法，以对测试目标实用程序的上下文敏感预测和对测试目标进行排名，并且（3）开发了一种评估概率方法，以评估在软件开发过程中遇到的一系列测试目标的代表性。除研究工具外，该项目还将提供公开可用的文物，以培训新一代的软件工程师。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力优点和更广泛影响的评估评估标准的评估值得支持的。