SHF: Small: Analyzing and Modeling Natural Language Usage in Software to Improve Software Maintenance Tools

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This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). Large scale software is difficult to maintain, modify and keep up to date. There is a dire need for automated support for software system navigation, search, and comprehension activities to assist software developers and maintainers. This project addresses this issue by tackling some foundational issues and providing innovative methods for useful automated support for software system navigation, search, and comprehension activities. The research will lead to automatic analyses of the programmer's words and their relationships, through their usage in code, which will elucidate the concepts and actions encoded in the program. By building the conceptual models from the ``sound bites'' using context and program structure, a more complete picture is recovered enabling transformative improvements in automated support for software maintenance and program comprehension.The analyses are heavily driven by natural language processing, information retrieval, and machine learning. The research will advance the theory and development of software maintenance tools, which will improve the effectiveness of software maintainers, decreasing software maintenance costs and raising software quality as software evolves. In developing a new required course on software engineering-in-the-small, the PIs will incorporate learning how to use and evaluate software maintenance tools, including those developed in this project.