Congratulations to Rahul for passing his second major examination for his PhD. Just one left!
Title: Inferring Semantic Information from Natural Language Specifications
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, I present 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.