CSR: Small: Automated Software Fault Localization via Static and Dynamic Analysis

该项目旨在通过使用静态和动态程序分析技术，在识别软件故障的根本原因方面提供自动帮助，从而改进对软件调试的工具支持。一个灵活的诊断框架首先确定故障的候选根本原因，然后使用一系列广泛的、开放式的诊断“过滤器”来缩小最可能的根本原因。一个关键的创新是，系统自动生成“通过”和“失败”的诊断输入，这些输入接近原始的失败输入，从而减少了遗漏根本原因的可能性。这项工作为这个困难的步骤开发了两种方法，基于concolic测试和使用应用程序的输入语法重写字符串，并研究了各种类型的程序属性（“可能的不变量”），以有效地定位各种类型的错误。然后使用基于现有和新的程序分析的新过滤器筛选候选人，以查明根本原因，并减少误报。这项研究将通过减少手工调试工作来显著提高程序员的工作效率，并通过减少修复关键软件故障的时间来提高可靠性和安全性。基于广泛使用的LLVM编译器的全自动诊断工具将公开分发给开发人员使用。它也将用于在大学课堂上教授调试技术和程序分析技术。

This project aims to improve tool support for software debugging, by providing automatic assistance in the identification of root causes of a software failure using static and dynamic program analysis techniques. A flexible diagnosis framework first identifies candidate root causes of a failure and then uses a broad, open-ended series of diagnosic "filters" to narrow down the most likely root causes. A key innovation is that the system automatically generates "passing" and "failing" inputs for diagnosis that are close to the original failing input, making it less likely that root causes are missed. The work develops two approaches for this difficult step, based on concolic testing and on string rewriting using the application's input grammar, and investigates various types of program properties ("likely invariants") for effective localization of various classes of bugs. Candidates are then winnowed using novel filters based on existing and new program analyses to pinpoint root causes with few false positives.This research will significantly increase programmer productivity by reducing manual debugging effort and improve reliability and security by reducing the time to fix critical software failures. A fully-automated diagnosis tool based on the widely-used LLVM compiler will be publicly distributed for developers to use. It will also be used for teaching debugging techniques and program analysis techniques in university classes.