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Byungjeong Lee
Department of Computer Science, University of Seoul, Seoul 02504, Korea

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Journal article
Published: 02 March 2021 in Symmetry
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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.

ACS Style

Geunseok Yang; Byungjeong Lee. Utilizing Topic-Based Similar Commit Information and CNN-LSTM Algorithm for Bug Localization. Symmetry 2021, 13, 406 .

AMA Style

Geunseok Yang, Byungjeong Lee. Utilizing Topic-Based Similar Commit Information and CNN-LSTM Algorithm for Bug Localization. Symmetry. 2021; 13 (3):406.

Chicago/Turabian Style

Geunseok Yang; Byungjeong Lee. 2021. "Utilizing Topic-Based Similar Commit Information and CNN-LSTM Algorithm for Bug Localization." Symmetry 13, no. 3: 406.

Journal article
Published: 02 April 2018 in Symmetry
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Owing to the high complexity of recent software products, developers cannot avoid major/minor mistakes, and software bugs are generated during the software development process. When developers manually modify a program source code using bug descriptions to fix bugs, their daily workloads and costs increase. Therefore, we need a way to reduce their workloads and costs. In this paper, we propose a novel automatic fault repair method by using similar bug fix information based on genetic programming (GP). First, we searched for similar buggy source codes related to the new given buggy code, and then we searched for a fixed the buggy code related to the most similar source code. Next, we transformed the fixed code into abstract syntax trees for applying GP and generated the candidate program patches. In this step, we verified the candidate patches by using a fitness function based on given test cases to determine whether the patch was valid or not. Finally, we produced program patches to fix the new given buggy code.

ACS Style

Geunseok Yang; Youngjun Jeong; Kyeongsic Min; Jung-Won Lee; Byungjeong Lee. Applying Genetic Programming with Similar Bug Fix Information to Automatic Fault Repair. Symmetry 2018, 10, 92 .

AMA Style

Geunseok Yang, Youngjun Jeong, Kyeongsic Min, Jung-Won Lee, Byungjeong Lee. Applying Genetic Programming with Similar Bug Fix Information to Automatic Fault Repair. Symmetry. 2018; 10 (4):92.

Chicago/Turabian Style

Geunseok Yang; Youngjun Jeong; Kyeongsic Min; Jung-Won Lee; Byungjeong Lee. 2018. "Applying Genetic Programming with Similar Bug Fix Information to Automatic Fault Repair." Symmetry 10, no. 4: 92.

Conference paper
Published: 20 December 2017 in Lecture Notes in Electrical Engineering
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As the software R&D project progresses, various software artifacts such as software test case, software test descriptions, software source codes, software design descriptions, and software requirements specification will inevitably be produced. When developer performs the system testing, software bugs will be found if there is fault in it. After that, developer will write up an incident report for fault management, and then developer will try to find the artifacts for checking and fixing. Because developer must check the requirement before the fixing and developer has to get the source code including the bug and its test case for fixing and unit testing. Therefore, useful approach should be proposed in order to trace the artifacts from incident report. In this paper, we propose a novel approach for recovering fault traceability links by using IR technique to show that our approach can be a useful solution.

ACS Style

Seungsuk Baek; Jung-Won Lee; Byungjeong Lee. Towards Recovering Fault Traceability Links by Using Information Retrieval Technique. Lecture Notes in Electrical Engineering 2017, 1180 -1185.

AMA Style

Seungsuk Baek, Jung-Won Lee, Byungjeong Lee. Towards Recovering Fault Traceability Links by Using Information Retrieval Technique. Lecture Notes in Electrical Engineering. 2017; ():1180-1185.

Chicago/Turabian Style

Seungsuk Baek; Jung-Won Lee; Byungjeong Lee. 2017. "Towards Recovering Fault Traceability Links by Using Information Retrieval Technique." Lecture Notes in Electrical Engineering , no. : 1180-1185.

Conference paper
Published: 20 December 2017 in Lecture Notes in Electrical Engineering
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In this paper, we propose the model for automated repair in software fault. Automated patch generation is the most important technique in these days. Genetic Programming (GP) technique is used for automatic program repair, but most of the techniques use just a source code including fault to make initial population. We propose two methods to select similar bug fixing history; using topic modeling and finding similar bugs by using code block similarity.

ACS Style

Youngjun Jeong; Kyeongsic Min; Geunseok Yang; Jung-Won Lee; Byungjeong Lee. Toward Providing Automatic Program Repair by Utilizing Topic-Based Code Block Similarity. Lecture Notes in Electrical Engineering 2017, 1257 -1262.

AMA Style

Youngjun Jeong, Kyeongsic Min, Geunseok Yang, Jung-Won Lee, Byungjeong Lee. Toward Providing Automatic Program Repair by Utilizing Topic-Based Code Block Similarity. Lecture Notes in Electrical Engineering. 2017; ():1257-1262.

Chicago/Turabian Style

Youngjun Jeong; Kyeongsic Min; Geunseok Yang; Jung-Won Lee; Byungjeong Lee. 2017. "Toward Providing Automatic Program Repair by Utilizing Topic-Based Code Block Similarity." Lecture Notes in Electrical Engineering , no. : 1257-1262.