토픽 모델과 버그 리포트 메타 필드를 이용한 버그 심각도 예측 방법

양근석(Geunseok Yang),이병정(Byungjeong Lee) 한국정보과학회 2015 정보과학회 컴퓨팅의 실제 논문지 Vol.21 No.9

최근 개발된 소프트웨어들은 많은 수의 컴포넌트들을 가지고 있으며, 복잡성 또한 증가하고 있다. 지난 해 오픈소스 프로젝트 (Eclipse, Mozilla)에서는 하루에 약 375건의 버그 리포트가 제출되었다. 이렇게 증가된 버그 리포트들로 인해 개발자들의 시간과 노력이 불필요하게 증가하고 있다. 또 버그 심각도는 품질 보증 담당자, 프로젝트 매니저 또는 개발자에 의해 직접 판단되므로 그들에 의해 주관적으로 결정된다. 또한 많은 수의 버그 리포트 때문에 심각도 판단에서 실수할 수도 있다. 따라서 본 논문에서는 버그 심각도 예측 방법을 제안한다. 먼저, 새로운 버그 리포트가 제출되면, 유사한 토픽을 찾아내고 버그 리포트의 메타 필드를 이용하여 후보 버그 리포트의 범위를 줄인다. 추출된 버그 리포트를 Naive Bayes Multinomial 기법에 훈련하여 새로운 버그 리포트의 심각성을 예측한다. 오픈소스 프로젝트에 본 방법을 적용하여 본 방법이 버그 심각도 예측에 효과적이라는 것을 보인다. 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.

토픽 모델과 소셜 네트워크를 이용한 개발자 추천방법

양근석(Geunseok Yang),장도(Tao Zhang),이병정(Byungjeong Lee) 한국정보과학회 2014 정보과학회논문지 : 소프트웨어 및 응용 Vol.41 No.8

최근 소프트웨어 규모가 더욱 커지고 복잡해지고 있다. 하루에도 수많은 버그 리포트들이 버그 저장소에 전송 되어 개발자들의 업무가 늘어나고 있다. 이러한 버그 리포트들을 적절한 개발자에게 전달하여 빠르고 정확하게 소프트웨어의 결함이 수정되어야 하는데, 많은 버그 리포트들이 적절하지 않는 개발자에게 배정되어 다른 개발자에게 다시 재배정 되는 경우가 빈번하게 일어나고 있다. 이것은 배정자가 전송 받은 버그 리포트들을 정확히 이해하지 못했거나, 또는 모든 개발자들의 능력을 바르게 파악하지 못해 발생한다. 이것은 소프트웨어 유지보수에 개발자의 시간과 노력을 많이 필요하게 한다. 이러한 문제를 해결하기 위해 본 연구에서는 버그 리포트와 관련된 토픽을 찾아내고, 토픽 내 개발자들의 소셜 네트워크 관계를 분석해서 적절한 개발자를 추천하는 기법을 제안한다. 그리고 공개 소스 프로젝트를 이용한 개발자 추천에 대한 성능비교 실험을 통하여 본 연구에서 제안한 방법이 효과적이라는 것을 보인다. Recently, software projects have been increasing and getting complex. Due to the large number of submitted bug reports, developers’ workload increases. Generally in bug triage process, the triagers assign the bug report to fixer (developer) in order to resolve the bug. However, bug reports have been reassigned to other developers because fixers are not suitable. This is why the triagers did not correctly check and understand the bug report and decide the appropriate developers to fix the bug. This results in increase of developers’ time and efforts in software maintenance. To resolve these problems, in this paper, we propose a novel method for developer recommendation based on topic model and social network. First, we build a basis of topic(s) from bug reports. Next, when a new bug report (test data set) comes, we select the most similar topic(s) and extract the participated developers from the topic(s). Finally, by applying social network, we analyze the developers’ behavior (comment and commit activity) and recommend the appropriate developers. In this paper we compare our work with related studies through performance experiments on open source projects. The results show that our approach is more effective than other studies in bug triage.

Applying Topic Modeling and Similarity for Predicting Bug Severity in Cross Projects

( Geunseok Yang ),( Kyeongsic Min ),( Jung-won Lee ),( Byungjeong Lee ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.3

Recently, software has increased in complexity and been applied in various industrial fields. As a result, the presence of software bugs cannot be avoided. Various bug severity prediction methodologies have been proposed, but their performance needs to be further improved. In this study, we propose a novel technique for bug severity prediction in cross projects such as Eclipse, Mozilla, WireShark, and Xamarin by using topic modeling and similarity (i.e., KL-divergence). First, we construct topic models from bug repositories in cross projects using Latent Dirichlet Allocation (LDA). Then, we find topics in each project that contain the most numerous similar bug reports by using a new bug report. Next, we extract the bug reports belonging to the selected topics and input them to a Naïve Bayes Multinomial (NBM) algorithm. Finally, we predict the bug severity in the new bug report. In order to evaluate the performance of our approach and to verify the difference between cross projects and single project, we compare it with the Naïve Bayes Multinomial approach; the Lamkanfi methodology, which is a well-known bug severity prediction approach; and an emotional similarity-based bug severity prediction approach. Our approach exhibits a better performance than the compared methods.

커밋 히스토리에 기반한 버그 및 커밋 연결 기법

채영재(Youngjae Chae),이은주(Eunjoo Lee) 한국정보과학회 2016 정보과학회 컴퓨팅의 실제 논문지 Vol.22 No.5

커밋-버그 링크는 커밋히스토리(commit history)와 버그 리포트(bug report) 간의 연결(Link)을 뜻한다. 커밋-버그 링크는 소프트웨어 유지보수와 결함 예측, 버그 추적 시스템(Bug Tracking System)에 이용이 되며, 특히 결함 예측 측면에서는 성능면에서의 기반이 된다. 일반적으로 링크를 자동으로 연결하는 방식은 텍스트 유사도(text similarity)나 시간 간격(time interval), 키워드(keyword) 등을 통해서 추출하였다. 하지만 기존 방식은 커밋히스토리(commit history)의 질적인 부분에 의존적이기 때문에 다수의 링크를 놓치게 된다는 단점이 존재한다. 본 논문에서는 커밋히스토리의 메시지(message)부분에만 의존하지 않고, 버그리포트에서 연결된 커밋히스토리의 파일간의 유사도를 이용하여 링크를 연결할 수 있는 방식을 제안하고 실험을 통하여 본 기법의 적용성을 보인다. ‘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

정확한 프로그램 결함 위치 추적을 위한 전-후처리 방법론

김동선(Dongsun Kim) 한국방송·미디어공학회 2022 방송공학회논문지 Vol.27 No.2

Tracking the location of program defects is an essential task for software maintenance and repair. When a bug report is submitted, bug localization is a costly task because of the developers manual effort. Many researchers have tried to automate the task, but according to the reported results, the performance is still insufficient in practice. Therefore, in this study, we analyzed a large amount of bug report data and the latest research and found that the existing studies used only one preprocessing without considering the characteristics of the bug report. In this paper, to solve the problems mentioned earlier, we propose a pre/post-processing operator selection approach for bug localization.

Systematic Review of Bug Report Processing Techniques to Improve Software Management Performance

( Dong-gun Lee ),( Yeong-seok Seo ) 한국정보처리학회 2019 Journal of information processing systems Vol.15 No.4

Bug report processing is a key element of bug fixing in modern software maintenance. Bug reports are not processed immediately after submission and involve several processes such as bug report deduplication and bug report triage before bug fixing is initiated; however, this method of bug fixing is very inefficient because all these processes are performed manually. Software engineers have persistently highlighted the need to automate these processes, and as a result, many automation techniques have been proposed for bug report processing; however, the accuracy of the existing methods is not satisfactory. Therefore, this study focuses on surveying to improve the accuracy of existing techniques for bug report processing. Reviews of each method proposed in this study consist of a description, used techniques, experiments, and comparison results. The results of this study indicate that research in the field of bug deduplication still lacks and therefore requires numerous studies that integrate clustering and natural language processing. This study further indicates that although all studies in the field of triage are based on machine learning, results of studies on deep learning are still insufficient.

Systematic Review of Bug Report Processing Techniques to Improve Software Management Performance

Lee, Dong-Gun,Seo, Yeong-Seok Korea Information Processing Society 2019 Journal of information processing systems Vol.15 No.4

Bug report processing is a key element of bug fixing in modern software maintenance. Bug reports are not processed immediately after submission and involve several processes such as bug report deduplication and bug report triage before bug fixing is initiated; however, this method of bug fixing is very inefficient because all these processes are performed manually. Software engineers have persistently highlighted the need to automate these processes, and as a result, many automation techniques have been proposed for bug report processing; however, the accuracy of the existing methods is not satisfactory. Therefore, this study focuses on surveying to improve the accuracy of existing techniques for bug report processing. Reviews of each method proposed in this study consist of a description, used techniques, experiments, and comparison results. The results of this study indicate that research in the field of bug deduplication still lacks and therefore requires numerous studies that integrate clustering and natural language processing. This study further indicates that although all studies in the field of triage are based on machine learning, results of studies on deep learning are still insufficient.

Systematic Review of Bug Report Processing Techniques to Improve Software Management Performance

이동건,서영석 한국정보처리학회 2019 Journal of information processing systems Vol.15 No.4

Bug report processing is a key element of bug fixing in modern software maintenance. Bug reports are notprocessed immediately after submission and involve several processes such as bug report deduplication andbug report triage before bug fixing is initiated; however, this method of bug fixing is very inefficient because allthese processes are performed manually. Software engineers have persistently highlighted the need to automatethese processes, and as a result, many automation techniques have been proposed for bug report processing;however, the accuracy of the existing methods is not satisfactory. Therefore, this study focuses on surveying toimprove the accuracy of existing techniques for bug report processing. Reviews of each method proposed inthis study consist of a description, used techniques, experiments, and comparison results. The results of thisstudy indicate that research in the field of bug deduplication still lacks and therefore requires numerous studiesthat integrate clustering and natural language processing. This study further indicates that although all studiesin the field of triage are based on machine learning, results of studies on deep learning are still insufficient.

소프트웨어 버그 정정에 SeqGAN 알고리즘을 적용

양근석 ( Geunseok Yang ),이병정 ( Byungjeong Lee ) 한국인터넷정보학회 2020 인터넷정보학회논문지 Vol.21 No.5

최근 소프트웨어가 다양한 분야에 적용되면서 소프트웨어 규모와 프로그램 코드의 복잡성이 증가하였다. 이에 따라 소프트웨어 버그의 존재가 불가피하게 발생하고, 소프트웨어 유지보수의 비용이 증가하고 있다. 오픈 소스 프로젝트에서는 개발자가 할당 받은 버그 리포트를 해결할 때 많은 디버깅 시간을 소요한다. 이러한 문제를 해결하기 위해 본 논문은 SeqGAN 알고리즘을 소프트웨어 버그 정정에 적용한다. 자세히는 SeqGAN 알고리즘을 활용하여 프로그램 소스코드를 학습한다. 학습과정에서 공개된 유사 소스코드도 같이 활용한다. 생성된 후보 패치에 대한 적합성을 평가 하기 위해 적합도 함수를 적용하고, 주어진 모든 테스트 케이스를 통과하면 소프트웨어 버그 정정이 되었다고 본다. 제안한 모델의 효율성을 평가하기 위해 베이스라인과 비교하였으며, 제안한 모델이 더 잘 정정하는 것을 보였다. Recently, software size and program code complexity have increased due to application to various fields of software. Accordingly, the existence of program bugs inevitably occurs, and the cost of software maintenance is increasing. In open source projects, developers spend a lot of debugging time when solving a bug report assigned. To solve this problem, in this paper, we apply SeqGAN algorithm to software bug repair. In detail, the SeqGAN model is trained based on the source code. Open similar source codes during the learning process are also used. To evaluate the suitability for the generated candidate patch, a fitness function is applied, and if all test cases are passed, software bug correction is considered successful. To evaluate the efficiency of the proposed model, it was compared with the baseline, and the proposed model showed better repair.

Enhancing Model-based Fault Traceability by Using Similarity between Bug and Commit Information

정동주,민경식,이정원,이병정 한국인터넷정보학회 2019 인터넷정보학회논문지 Vol.20 No.2

As software development technology evolves, the quality of software has increased. But software created through sophisticated technology is still defective. The developer will be aware of the defect through a bug report and the reported defect must be fixed as soon as possible for the software to function correctly. It is important to know which component of the program is related to the reported defect and should be fixed. However, even though the developer understands the developed software, the task of tracing faults is a time-consuming task and requires effort. Therefore, if there is a way for developers to support tracing faults, they could fix defects more quickly. Because fixing defects rapidly is a factor of software reliability, fault traceability is essential and an effective method is needed. Therefore, in this paper, we propose a model-based fault traceability enhancement technique by using bug report and commit information and verify the effectiveness of the proposed technique.