Automatics Natural-Language Fault Diagnoses

The overall debugging process is a complicated and troublesome task, involving several stagesand dimensions of human comprehension. Developers seek understanding of several aspectsof faults, such as, where the faults are located in the code, what sequences of actions invokefaults that cause failures, and why the program is failing due to the faults. Despite a largebody of research for providing automation for the first two tasks, very little work has beenconducted in helping to assist in the last question of "why" -- that is, for describing thenature of the fault. I propose an automated approach to describe software faults that canindicate the nature of faults and their failures; thus ameliorating comprehension and reducingmanual effort. To create this automated approach I propose using a combination of dynamicanalysis techniques with information retrieval and text mining to generate natural languageclues. In this document I outline the design of my technique along with a research plan thatI have used to investigate the effectiveness of using a such an approach. In particular, I detailfive evaluations that provide a thorough assessment of the relative merits and shortcomingsof the proposed technique