• Journal of Broadcast Engineering
• /
• v.27 no.2
• /
• pp.240-243
• /
• 2022

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 developer's 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.

• Journal of KIISE
• /
• v.44 no.8
• /
• pp.832-841
• /
• 2017

Bug reports are essential documents for developers to localize and fix bugs. These reports contain information regarding software bugs or failures that occur during software operation and maintenance phase. Information Retrieval-based Bug Localization (IR-BL) techniques have been proposed to reduce the time and cost it takes for developers to resolve bug reports. However, if a low-quality bug report is submitted, the performance of such techniques can be significantly degraded. To address this problem, we propose a quality prediction method that selects low-quality bug reports. This process; defines a Quality property of a Bug report as a Query (Q4BaQ) and predicts the quality of the bug reports using machine learning. We evaluated the proposed method with 3 open source projects. The results of the experiment show that the proposed method achieved an average F-measure of 87.31% and outperformed previous prediction techniques by up to 6.62% in the F-measure. Finally, a combination of the proposed method and traditional automatic query reformulation method improved the MRR and MAP by 0.9% and 1.3%, respectively.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2018.10a
• /
• pp.517-520
• /
• 2018

보안 버그는 소프트웨어의 치명적인 취약점을 노출해 제품의 질 저하 및 정보유출을 일으킨다. 위 상황을 최소화하기 위해 보안 버그 추적 기술이 필요하다. 본 논문에서는 보안 버그가 발생한 소스 파일의 특징을 분석하여 보안 버그 추적을 위한 정보를 제공한다. 우리는 보안이 중요하게 다루어져야 하는 안드로이드와 블록체인 오픈소스를 대상으로 보안 버그 리포트를 수집해 보안 버그가 나타난 소스 파일의 텍스트를 분석했다. 분석 결과, 안드로이드의 경우 통신 관련 패키지에 포함된 파일에서 보안 버그가 발생했다. 블록체인의 경우 계정, 키 저장 관련 파일들에서 보안 버그가 주로 발생했다. 보안 버그 추적 시 본 연구의 분석 결과를 반영한다면 빠르고 정확하게 보안 버그 파일을 찾을 수 있을 것으로 보인다.