Abstract: Because software systems are large and complex, developers often use software tools to understand unfamiliar code. In turn, software tools often utilize information about the program in the form of various program representations, which can provide detailed program information. Because traditional program representations do not capture the natural language clues in code, they often fail to assist the developer during high-level program understanding tasks. To bridge the gap between current software tools and the software developers' high-level questions, we propose supplementing traditional program representations with a natural language representation that exploits the information embedded in the program's names and comments. This talk will discuss the creation of a natural language program representation of source code, the use of this representation in two software maintenance tools, and the evaluation of these tools, all of which help build the case that natural language clues could benefit a variety of software tools.

Short Bio: David Shepherd has just joined Gail Murphy's lab at the University of British Columbia as a postdoctoral fellow after completing his Ph.D. at the University of Delaware under the advisement of Lori Pollock and K. Vijay-Shanker. He has co-organized several workshops, including LATE@AOSD (05, 06, 07), which he co-founded. His research contributions have been in the area of aspect mining and concern location, centered around the AOSD community. His professional experience complements his academic interests, as he has been a research intern at the IBM T. J. Watson lab and Quantum Leap Innovations.