Journal
SYMMETRY-BASEL
Volume 13, Issue 3, Pages -Publisher
MDPI
DOI: 10.3390/sym13030406
Keywords
bug localization; bug report; deep learning
Categories
Funding
- National Research Foundation of Korea(NRF) - Korea government(MSIT) [2020R1A2B5B01002467]
- National Research Foundation of Korea [2020R1A2B5B01002467] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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The study presents a bug localization method using topic-based similar commit information, extracting similar bug reports and commit information based on topics, and enhancing model performance by extracting and classifying shared features between similar source codes before training. The method successfully detects and recommends buggy source code files, showing good performance in comparison with open-source project code.
With the use of increasingly complex software, software bugs are inevitable. Software developers rely on bug reports to identify and fix these issues. In this process, developers inspect suspected buggy source code files, relying heavily on a bug report. This process is often time-consuming and increases the cost of software maintenance. To resolve this problem, we propose a novel bug localization method using topic-based similar commit information. First, the method determines similar topics for a given bug report. Then, it extracts similar bug reports and similar commit information for these topics. To extract similar bug reports on a topic, a similarity measure is calculated for a given bug report. In the process, for a given bug report and source code, features shared by similar source codes are classified and extracted; combining these features improves the method's performance. The extracted features are presented to the convolutional neural network's long short-term memory algorithm for model training. Finally, when a bug report is submitted to the model, a suspected buggy source code file is detected and recommended. To evaluate the performance of our method, a baseline performance comparison was conducted using code from open-source projects. Our method exhibits good performance.
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