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Locating bugs is an important, but effort-intensive and time-consuming task,when dealing with large-scale systems. To address this, Information Retrieval(IR) techniques are increasingly being used to suggest potential buggy sourcecode locations, for given bug reports. While IR techniques are very scalable,in practice their effectiveness in accurately localizing bugs in a softwaresystem remains low. Results of empirical studies suggest that the effectivenessof bug localization techniques can be augmented by the configuration of queriesused to locate buggy code. However, in most IR-based bug localizationtechniques, presented by researchers, the impact of the queries' configurationsis not fully considered. In a similar vein, techniques consider all codeelements as equally suspicious of being buggy while localizing bugs, but thisis not always the case either.In this paper, we present a new method-level,information-retrieval-based bug localization technique called ``BoostNSift''.BoostNSift exploits the important information in queries by `boost'ing thatinformation, and then `sift's the identified code elements, based on a noveltechnique that emphasizes the code elements' specific relatedness to a bugreport over its generic relatedness to all bug reports. To evaluate theperformance of BoostNSift, we employed a state-of-the-art empirical design thathas been commonly used for evaluating file level IR-based bug localizationtechniques: 6851 bugs are selected from commonly used Eclipse, AspectJ, SWT,and ZXing benchmarks and made openly available for method-level analyses.

BoostNSift: A Query Boosting and Code Sifting Technique for Method Level Bug Localization