Sangeeta IIITD MTech Thesis

Bug or Fault localization is a process of identifying the specific location(s) or region(s) of source code (at various granularity levels such as the directory path, file, method or statement) that is faulty and needs to be modified to repair the defect. Bug localization is a routine task in software maintenance (corrective maintenance). Due to the increasing size and complexity of current software applications, automated solutions for bug localization can significantly reduce human effort and software maintenance cost. We presented a technique (which falls into the class of static techniques for bug localization) for bug localization using a character N-gram based Information Retrieval (IR) model. We framed the problem of bug localization as a relevant document(s) search task for a given query and investigated the application of character-level N-gram based textual features derived from bug reports and source-code file attributes. We implemented the proposed IR model and evaluated its performance on dataset downloaded from two popular open-source projects (JBOSS and Apache). We conducted a series of experiments to validate our hypothesis and presented evidences to demonstrate that the proposed approach is effective. The accuracy of the proposed approach is measured in terms of the standard and commonly used SCORE and MAP (Mean Average Precision) metrics for the task of bug localization. Experimental results reveal that the median value for the SCORE metric for JBOSS and Apache dataset is 99.03% and 93.70% respectively. We observed that for 16.16% of the bug reports in the JBOSS dataset and for 10.67% of the bug reports in the Apache dataset, the average precision value (computed at all recall levels) is between 0.9 and 1.0. KeywordsBug Localization, Mining Software repositories (MSR), Information Retrieval (IR), Automated Software engineering (ASE)