• KIISE Transactions on Computing Practices
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• v.22 no.5
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• pp.235-239
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• 2016

'Commit-bug link', the link between commit history and bug reports, is used for software maintenance and defect prediction in bug tracking systems. Previous studies have shown that the links are automatically detected based on text similarity, time interval, and keyword. Existing approaches depend on the quality of commit history and could thus miss several links. In this paper, we proposed a technique to link commit and bug report using not only messages of commit history, but also the similarity of files in the commit history coupled with bug reports. The experimental results demonstrated the applicability of the suggested approach.

• Journal of the Korea Society of Computer and Information
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• v.19 no.9
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• pp.103-115
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• 2014

In this paper, we propose UniBAS(Unified Bug Analysis System) to provide a unified repository model by integrating the extracted data from the heterogeneous bug tracking systems. The UniBAS reduces the cost and complexity of the MSR(Mining Software Repositories) research process and enables the researchers to focus on their logics rather than the tedious and repeated works such as extracting repositories, processing data and building analysis models. Additionally, the system not only extracts the data but also automatically generates database tables, views and stored procedures which are required for the researchers to perform query-based analysis easily. It can also generate various types of exported files for utilizing external analysis tools or managing research data. A case study of detecting duplicate bug reports from the Firfox project of the Mozilla site has been performed based on the UniBAS in order to evaluate the usefulness of the system. The results of the experiments with various algorithms of natural language processing and flexible querying to the automatically extracted data also showed the effectiveness of the proposed system.

• KIISE Transactions on Computing Practices
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• v.21 no.9
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• pp.616-621
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• 2015

Recently developed software systems have many components, and their complexity is thus increasing. Last year, about 375 bug reports in one day were reported to a software repository in Eclipse and Mozilla open source projects. With so many bug reports submitted, developers' time and efforts have increased unnecessarily. Since the bug severity is manually determined by quality assurance, project manager or other developers in the general bug fixing process, it is biased to them. They might also make a mistake on the manual decision because of the large number of bug reports. Therefore, in this study, we propose an approach of bug severity prediction to solve these problems. First, we find similar topics within a new bug report and reduce the candidate reports of the topic by using the meta field of the bug report. Next, we train the reduced reports by applying Naive Bayes Multinomial. Finally, we predict the severity of the new bug report. We compare our approach with other prediction algorithms by using bug reports in open source projects. The results show that our approach better predicts bug severity than other algorithms.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.12
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• pp.511-522
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• 2014

During the development of the software, a variety of bugs are reported. Several bug tracking systems, such as, Bugzilla, MantisBT, Trac, JIRA, are used to deal with reported bug information in many open source development projects. Bug reports in bug tracking system would be triaged to manage bugs and determine developer who is responsible for resolving the bug report. As the size of the software is increasingly growing and bug reports tend to be duplicated, bug triage becomes more and more complex and difficult. In this paper, we present an approach to assign bug reports to appropriate developers, which is a main part of bug triage task. At first, words which have been included the resolved bug reports are classified according to each developer. Second, words in newly bug reports are selected. After first and second steps, vectors whose items are the selected words are generated. At the third step, TF-IDF(Term frequency - Inverse document frequency) of the each selected words are computed, which is the weight value of each vector item. Finally, the developers are recommended based on the similarity between the developer's word vector and the vector of new bug report. We conducted an experiment on Eclipse JDT and CDT project to show the applicability of the proposed approach. We also compared the proposed approach with an existing study which is based on machine learning. The experimental results show that the proposed approach is superior to existing method.