• Journal of Information Processing Systems
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• 제15권4호
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• pp.967-985
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• 2019

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.

• Journal of Information Processing Systems
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• 제14권1호
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• pp.129-139
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• 2018

The crowdsourcing software development (CSD) is growing rapidly in the open call format in a competitive environment. In CSD, tasks are posted on a web-based CSD platform for CSD workers to compete for the task and win rewards. Task searching and assigning are very important aspects of the CSD environment because tasks posted on different platforms are in hundreds. To search and evaluate a thousand submissions on the platform are very difficult and time-consuming process for both the developer and platform. However, there are many other problems that are affecting CSD quality and reliability of CSD workers to assign the task which include the required knowledge, large participation, time complexity and incentive motivations. In order to attract the right person for the right task, the execution of action plans will help the CSD platform as well the CSD worker for the best matching with their tasks. This study formalized the task assignment method by utilizing different situations in a CSD competition-based environment in artificial intelligence (AI) planning. The results from this study suggested that assigning the task has many challenges whenever there are undefined conditions, especially in a competitive environment. Our main focus is to evaluate the AI automated planning to provide the best possible solution to matching the CSD worker with their personality type.

• 한국정보통신학회논문지
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• 제9권1호
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• pp.172-180
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• 2005

동적 조각화 기법은 프로그램을 이해하기 쉬운 조각단위로 분해하여 소프트웨어 개발자나 유지보수자가 프로그램을 쉽게 이해할 수 있도록 지원한다. 본 논문에서는 프로그램을 조각화하는데 걸리는 시간을 단축할 수 있는 동적 조각화 알고리즘을 제안하였다. 모든 문장에 대해 배정연산자를 기준으로 우측에서 사용되는 참조변수집합과 좌측에서 사용되어 변수의 값에 영향을 미치는 변경변수집합을 산출하고, 변수선어부분의 모든 변수에 대해 변수간 관련성(VV)을 작성하였다. 이를 활용하여 제안한 알고리즘을 적용하면 동적조각을 추출할 수 있다. 제안한 알고리즘의 성능을 기존방법과 비교한 결과 기준변수의 개수가 3개 이상인 경우에는 문장의 평균 비교횟수를 35% 감소시킴으로써 동적조각을 추출하는 과정에 소요되는 시간을 단축시킬 수 있었다.