Understanding and Fixing Complex Faults in Embedded Cyberphysical Systems

Automated Fixing of Programs with Contracts

This paper describes AutoFix, an automatic debugging technique that can fix faults in general-purpose software. To provide high-quality fix suggestions and to enable automation of the whole debugging process, AutoFix relies on the presence of simple specification elements in the form of contracts (such as pre- and postconditions). Using contracts enhances the precision of dynamic analysis techniques for fault detection and localization, and for validating fixes. The only required user input to the AutoFix supporting tool is then a faulty program annotated with contracts; the tool produces a co…

The Effect of Noise on Sofware Engineers' Performance

Background: Noise, defined as an unwanted sound, is one of the commonest factors that could affect people's performance in their daily work activities. The software engineering research community has marginally investigated the effects of noise on software engineers' performance. Aims: We studied if noise affects software engineers' performance in (i) comprehending functional requirements and (ii) fixing faults in the source code. Method: We conducted two experiments with final-year undergraduate students in Computer Science. In the first experiment, we asked 55 students to comprehend function…

CADET: Debugging and Fixing Misconfigurations using Counterfactual Reasoning

Modern computing platforms are highly-configurable with thousands of interacting configurations. However, configuring these systems is challenging. Erroneous configurations can cause unexpected non-functional faults. This paper proposes CADET (short for Causal Debugging Toolkit) that enables users to identify, explain, and fix the root cause of non-functional faults early and in a principled fashion. CADET builds a causal model by observing the performance of the system under different configurations. Then, it uses casual path extraction followed by counterfactual reasoning over the causal mod…

更好的调试：结合静态分析与LLM的可解释崩溃故障定位

Better Debugging: Combining Static Analysis and LLMs for Explainable Crashing Fault Localization

本文提出结合静态分析与LLM的崩溃故障定位方法CrashTracker，通过提取异常抛出摘要和候选信息摘要，在Android框架崩溃定位中实现0.91的MRR，并显著提升解释的用户满意度。

机器学习系统调试技术的系统性综述

A Systematic Survey on Debugging Techniques for Machine Learning Systems

本文对机器学习系统的调试技术进行系统性综述，构建了故障与调试方法的分类法，发现仅48%的调试挑战已被解决，识别出13个主要挑战，揭示了研究与实际应用之间的显著差距。

DebugHarness: 模拟人类动态调试以实现自主程序修复

DebugHarness: Emulating Human Dynamic Debugging for Autonomous Program Repair

DebugHarness通过模拟人类动态调试过程，利用LLM实现复杂漏洞的自动修复，其动态调试方法在SEC-bench数据集上实现了90%的修复率，比现有方法提升30%以上。