Pangolin: An SFLBased Toolset for Feature Localization Pinpointing the location where a given unit of functionalityor featurewas implemented is a demanding and timeconsuming task, yet prevalent in most software maintenance or evolution efforts. To that extent, we present PANGOLIN, an Eclipse plugin that helps developers identifying features among the source code. It borrows Spectrumbased Fault Localization techniques from the software diagnosis research field by framing feature localization as a diagnostic problem. PANGOLIN prompts users to label system executions based on feature involvement, and subsequently presents its spectrumbased feature localization analysis to users with the aid of a colorcoded, hierarchic, and navigable visualization which was shown to be effective at conveying diagnostic information to users. Our evaluation shows that PANGOLIN accurately pinpoints feature implementations and is resilient to misclassifications by users. The tool can be downloaded at https:tqrg.github.iopangolin.

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.