A NOVEL INVARIANTS-BASED APPROACH FOR AUTOMATED SOFTWARE FAULT LOCALIZATION BY

Software bugs are everywhere. Not only do they infest software during development, but they escape our extermination efforts and enter production code. In addition to severe frustration to customers, software failures result in billions of dollars of lost revenue to service providers. The most important steps for debugging and eliminating a software failure are reproducing the failure and finding its root cause, either during development time or during production run. Currently, debugging is a costly, time-consuming and manual process. Automating some of these steps will greatly help developers, reduce costs, increase productivity and software reliability. In this thesis proposal, I propose a novel way of doing automated software bug diagnosis. Reproducing bug symptoms is a prerequisite for performing automatic bug diagnosis. Do bugs have characteristics that ease or hinder automatic bug diagnosis? As a first step, we conducted a thorough empirical study of several key characteristics of bugs that affect reproducibility at the production site [1]. We manually examined 266 randomly selected bug reports of six server applications and consider their implications on automatic bu