부산대학교 도서관

Software maintainers often use reverse engineering tools to aid in the extremely difficult task of understanding unfamiliar code, especially within large, complex software systems. While traditional program analysis can provide detailed information for reverse engineering, often this information is not sufficient to assist the user with high-level program understanding tasks. To bridge the gap between current reverse engineering tools and the high-level questions that software maintainers want answered, we propose supplementing traditional program analysis with natural language analysis of program source code. This paper presents a case study where we have augmented an existing reverse engineering tool, an aspect miner, to complement the existing traditional program analysis-based miner with natural language analysis of method names, class names, and comments. Our quantitative and qualitative results strongly suggest that supplementing traditional program analysis with natural language analysis is a promising approach to raising the level of effectiveness of reverse engineering tools.