CAREER: Scalable Automated Software Testing and Repair

题目：职业：可扩展自动化软件测试与修复PI: Koushik Sen摘要：当今的软件系统可靠性差，软件错误每年给美国经济造成600亿美元以上的损失。测试是工业上保证软件质量的主要技术。现有的测试生成技术，如随机测试和基于测试生成的符号执行，要么是无效的，要么是不可伸缩的。这个项目研究了一些技术，通过在实际技术（如测试）和数学上严格的技术（如模型检查和符号分析）之间架起桥梁，使自动化测试生成和自动化错误修复快速、可伸缩和详尽。具体来说，该项目整合了随机算法、符号分析和模型检查以及计算机器学习的思想，并在三个研究方面发展了新的思想：(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；(3)研究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，这些变体可以潜在地修复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bug修复消耗了软件开发总成本的一半以上。

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.