Wednesday, July 27, 2016 at 4:00 PM in Rice 504
Advisor: Baishakhi Ray
Committee Members: Hongning Wang
Title: Improving Spectrum-based Fault Localization using Unnaturalness of Bug
ABSTRACT: Debugging is an important but costly process during software development. Various techniques has been proposed to predict suspicious entities in the source code to assist debugging process. Such bug prediction methodologies, in general, assign suspicious scores to each program entity and generate a ranking based on the score to identify the buggy lines. For example, since buggy code has unusual behavior, they are usually considered “irregular” from the source code perspective. Natural Language Processing techniques can help evaluate this “irregularity” by building language model upon source code. On the other hand, program spectrum predicts defects by dynamic slicing—it records execution information of a program during tests, the lines caused test failures are more likely to be buggy. Both techniques generate a “suspicious” score (or ranking) for each program element, however, either technique only considered one aspect of the problem and have their own limitations. This project explores a joint analyzation of the results of these two types of software fault localization methods in order to improve the buggy line ranking results.