CAREER: Scalable Automated Software Testing and Repair

职业：可扩展的自动化软件测试和修复

• 批准号: 0747390
• 负责人: Koushik Sen
• 金额: $ 40万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-01 至 2014-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0747390&HistoricalAwards=false
• 关键词: CAREER; Scalable; Automated; Software; Testing

Proposal number: CCF-0747390 TITLE: CAREER: Scalable Automated Software Testing and Repair PI: Koushik Sen Abstract: Today's software systems suffer from poor reliability, with software errors costing the U.S. economy upwards of $60 billion annually. Testing is the predominant technique in industry to ensure software quality. Existing test generation techniques, such as random testing and symbolic execution based test generation, are either not effective or not scalable. This project investigates techniques to make automated test generation and automated bug fixing fast, scalable, and exhaustive by bridging the gap between practical techniques, such as testing, and mathematically rigorous techniques, such as model checking and symbolic analysis. Specifically, the project integrates ideas from randomized algorithms, symbolic analysis and model checking, and computational machine learning and develops novel ideas in three research efforts: (1) develop techniques for fast and exhaustive unit test generation; (2) scale automated testing to large software through compositional reasoning; and (3) investigate techniques for automated repair advice generation where automated test generation is used to automatically generate candidate program variants that could potentially fix the bugs discovered during testing. This project will immediately benefit the software industry where testing and bug fixing consume more than half of the total software development cost.

提案编号：CCF-0747390标题：职业：可扩展的自动化软件测试和修复PI：Koushik Sen摘要：今天的软件系统的可靠性很差，软件错误每年给美国经济造成600亿美元以上的损失。 测试是工业界保证软件质量的主要技术。 现有的测试生成技术，如随机测试和基于符号执行的测试生成，要么是不有效的或不可扩展的。 本项目研究的技术，使自动化测试生成和自动化错误修复快速，可扩展，并通过弥合差距之间的差距，如测试，数学严格的技术，如模型检查和符号分析。 具体来说，该项目整合了随机算法、符号分析和模型检测以及计算机器学习的思想，并在三个研究工作中开发了新的想法：（1）开发快速和详尽的单元测试生成技术;（2）通过组合推理将自动化测试扩展到大型软件;以及（3）研究用于自动修复建议生成的技术，其中自动测试生成用于自动生成候选程序变体，所述候选程序变体可以潜在地修复在测试期间发现的错误。 这个项目将立即使软件行业受益，因为测试和错误修复消耗了软件开发总成本的一半以上。