• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.866-869
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• 2020

소프트웨어 개발 환경이 빠르게 변화함에 따라 시스템의 복잡성이 증가하고 있다. 이에 따라 크고 작은 소프트웨어의 버그를 피할 수 없게 되며 이를 효율적으로 처리하기 위해 Bug report 를 사용한다. 하지만, Bug report 에서 개발자가 해당 Bug report 의 우선순위를 결정하는 과정은 노력과 비용 그리고 시간을 많이 소모하게 만든다. 따라서, 본 논문에서는 Bug report 내의 Stack trace 를 기반으로 Bug 의 우선순위를 자동적으로 추천하는 기법을 제안한다. 이를 위해 본 연구에서는 첫 번째로 Bug report 로부터 Stack trace 를 추출하였으며 Stack trace 의 3 가지 요소(Exception, Reason 그리고 Stack frame)에 TF-IDF, Word2Vec 그리고 Stack overflow 를 사용하여 특징 벡터를 정의하였다. 그리고 Bug 의 우선순위 추천 모델을 생성하기 위해 4 가지의 Classification 알고리즘을(Random Forest, Decision Tree, XGBoost, SVM)을 적용하였다. 평가에서는 266,292 개의 JDK library 의 Bug report 데이터를 수집하였고 그중 Stack trace 를 가진 Bug report 로부터 68%의 정확도를 산출하였다.

• Journal of KIISE
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• v.42 no.6
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• pp.755-763
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• 2015

As fixing bugs is a large part of software development and maintenance, estimating the time to fix bugs -bug fixing time- is extremely useful when planning software projects. Therefore, in this study, we propose a way to estimate bug fixing time using bug reports. First, we classify previous bug reports with meta fields by applying a k-NN method. Next, we compute the similarity of the new bug and previous bugs by using data from bug reports. Finally, we estimate how long it will take to fix the new bug using the time it took to repair similar bugs. In this study, we perform experiments with open source software. The results of these experiments show that our approach effectively estimates the bug fixing time.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.5
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• pp.235-241
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• 2015

Nowadays, research and industry on the internet of things is rapidly developing. Bug fixed field of the Software development related internet of things is a very important things. In this study, we analyze the properties that can affect what the bug fix-time by analyzing the time required to fix a bug associated with the Internet of Things. Using the k-NN classification method based on the attribute information to be classified as bug reports. Extracts a bug report based on the results of a similar property. Bug fixed by calculating the time of a similar bug report predicts the fix-time for new bugs. Depending on the prediction of the properties that affect the bug correction time, the properties of os, component, reporter, and assignee showed the best prediction accuracy.

• 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.

• Smart Media Journal
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• v.10 no.2
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• pp.76-83
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• 2021

With the development and use of smart devices such as smartphones and tablets increases, the mobile application market based on mobile devices is growing rapidly. Mobile application users write reviews to share their experience in using the application, which can identify consumers' various needs and application developers can receive useful feedback on improving the application through reviews written by consumers. However, there is a need to come up with measures to minimize the amount of time and expense that consumers have to pay to manually analyze the large amount of reviews they leave. In this work, we propose to collect delivery application user reviews from Google PlayStore and then use machine learning and deep learning techniques to classify them into four categories like application feature advantages, disadvantages, feature improvement requests and bug report. In the case of the performance of the Hugging Face's pretrained BERT-based Transformer model, the f1 score values for the above four categories were 0.93, 0.51, 0.76, and 0.83, respectively, showing superior performance than LSTM and GRU.