Source Code Retrieval for Bug Localization using Bug Report Bug localization helps software developers to track postreleased faulty source files with the help of userxe2x80x99s reported bug files. Information retrieval (IR) based bug localization have been widely used in recent years. It recommends relevant faulty source files to fix according to their highest similarity scores. We propose a combined approach of IRbased bug localization by operating previously fixed bug reports and source code structure. From the query, bug report structure is also considered to get more accurate faulty source files. In our approach, three parts in the source code file and two parts in bug report are combined as six combinations score. In some bug localization approach, features are usually linearly combined. Our approach uses linearly combine with the weight value. We perform experiments on three projects, i.e. SWT, AspectJ, and Eclipse. The result shows that the proposed approach achieves the relevance accuracy for bug localization process. According to the evaluation result, using the structure is more localized than no structured approach.

Combining SpectrumBased Fault Localization and Statistical Debugging: An Empirical Study Program debugging is a timeconsuming task, and researchers have proposed different kinds of automatic fault localization techniques to mitigate the burden of manual debugging. Among these techniques, two popular families are spectrumbased fault localization (SBFL) and statistical debugging (SD), both localizing faults by collecting statistical information at runtime. Though the ideas are similar, the two families have been developed independently and their combinations have not been systematically explored. In this paper we perform a systematical empirical study on the combination of SBFL and SD. We first build a unified model of the two techniques, and systematically explore four types of variations, different predicates, different risk evaluation formulas, different granularities of data collection, and different methods of combining suspicious scores. Our study leads to several findings. First, most of the effectiveness of the combined approach contributed by a simple type of predicates: branch conditions. Second, the risk evaluation formulas of SBFL significantly outperform that of SD. Third, finegrained data collection significantly outperforms coarsegrained data collection with a little extra execution overhead. Fourth, a linear combination of SBFL and SD predicates outperforms both individual approaches. According to our empirical study, we propose a new fault localization approach, PREDFL (Predicatebased Fault Localization), with the best configuration for each dimension under the unified model. Then, we explore its complementarity to existing techniques by integrating PREDFL with a stateoftheart fault localization framework. The experimental results show that PREDFL can further improve the effectiveness of stateoftheart fault localization techniques. More concretely, integrating PREDFL results in an up to 20.8% improvement w.r.t the faults successfully located at Top1, which reveals that PREDFL complements existing techniques.