BLESER: Bug Localization Based on Enhanced Semantic Retrieval

Zou, Weiqin; Li, Enming; Fang, Chunrong

doi:10.48550/arxiv.2109.03555

Cited by 1 publication

(1 citation statement)

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“…Another technique called Blesser [42]that get the source abstract syntax trees and code embedding. They tested their work on 5 C++ source code projects.…”

Section: B Machine Learning Approachesmentioning

confidence: 99%

Enhancing Bug Localization Using Phase-Based Approach

et al. 2023

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Software bug localization is an important step in the software maintenance process. Automatic bug localization can reduce the time consumed in the process of localization. Some techniques are applied in the bug localization process, but those techniques suffer from limitations in time and accuracy. This paper proposes a phase-based bug localization approach to overcome these limitations. The approach is composed of three main phases which are raw data preparation, package classification, and source code recommendation. The main input to our approach is a bug report and the source code of the past versions for the target system of interest. From the bug report, various information is utilized: the summary, the description, the stack traces, and the fixed source code files. The raw data preparation phase is used to restructure those inputs. The package classification phase aims to locate the package that would include the source code to be modified as a first step, hence reducing the time needed to locate the source code file due to the lexical mismatch between those files and the bug report data. Bidirectional Encoder Representations from Transformers (BERT), which is a sentence embedding technique, is utilized in the package classification and source code recommendation phases. The experimental results show that our approach outperforms existing approaches according to TOP-N rank and Mean Reciprocal Rank (MRR) evaluation metrics.

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“…Another technique called Blesser [42]that get the source abstract syntax trees and code embedding. They tested their work on 5 C++ source code projects.…”

Section: B Machine Learning Approachesmentioning

confidence: 99%