Inferring Semantic Information from Natural-Language Software Artifacts

Code-level specifications play an important role in software engineering. In addition to guiding the development process by outlining what/how to reuse, specifications also help in verification process by allowing quality assurance practitioners to test the expected outcome. One of the valuable source of such specifications are the Natural language API documents. However, sometimes humans often overlook these documents and build software systems that are inconsistent with specifications described in those documents. While there are tools and frameworks available to assist humans to build/reuse quality software, these tools are not designed to work on specifications in natural language. To address this issue, this report presents a Natural Language Processing (NLP) framework to automate the task of inferring semantic information from natural language software artifacts to bridge the disconnect between the inputs required by software engineering tools/frameworks and the specifications described in natural language. This report is a part of a larger dissertation goal to improve developer / tester / end-user productivity by automatically inferring semantic information from the textual descriptions in software artifacts. Specifically, in this report I present two recent research efforts that I have conducted in developing/applying NLP techniques for inferring semantics from natural language software artifacts. Furthermore, I also outline my dissertation plan along with intermediate deliverables to achieve my goal.