DependLoc: A Dependency-based Framework For Bug Localization

As software systems are becoming larger and more complex, debugging poses great challenges to software developers and maintainers. Among various efforts on easing the burden of debugging, bug localization techniques are developed to help locate where a bug occurs in source code files automatically. Information retrieval and deep neural network techniques are often adopted in existing research to achieve bug localization through capturing the textual or semantic similarity between bug reports and source code files. At the same time, some domain- specific eatures in software engineering are also utilized to locate the buggy files. However, the dependency relationship between classes (A depends on $B$ if $A$ references B) is not considered or utilized by existing approaches. In this work, we propose a novel framework DependLoc for bug localization which leverages the dependency relationship among source code files. DependLoc is based on the observation that buggy files may not be highly similar to a bug report but have a dependency relationship with one or more files that are quite similar to the bug report. DependLoc adopts a customized Ant Colony algorithm to quantify the intrinsic dependency relationship (called reference heat) and designs a segment-based encoder to learn this feature. Experimental results on six widely-used benchmark datasets for bug localization show that our approach outperforms the state-of-the-art methods, and Accuracy@10 is improved by 4% on average.