A Survey on Source Code Retrieval for Bug Localization Using Latent Dirichlet Allocation, Semantic Similarity and Code Smell Detection

Kamaraj Natraj, A.V Ramani

Abstract


Bug localization is the task of determining the source code which entities are relevant to a bug report. It is an important task of classification in software data set resources. Manual bug localization is labour intensive since developers must consider thousands of source code entities. To builds bug localization classifiers, based on information retrieval models, to locate entities that are textually similar to the bug report. Current research, however, does not consider the effect of classifier configuration. Designer uses data to detect a bug located in the segment of the source code to correct the bug. They are several categories are used, i.e., code smells detection and pattern clustering. To identify the numerous semantic relations existing between two given words, a pattern clustering algorithm has been proposed. This survey proposed research analysis on the bug localization classifiers based on information retrieval models to locate entities that are textually alike to the bug report.


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References


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DOI: https://doi.org/10.23956/ijarcsse.v8i8.832

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