Case Study for Semantic Search in Requirements Specification

Requirements engineering is an integral part of industrial engineering processes, which provides requirements specification in the form of technical documentation. These documents utilize technical natural language which is not very common for other natural language documents. Moreover, tracing or inter-connectivity of requirements is a common practice, which is usually not found in other natural language documents. In this thesis we create a case study to understand requirements engineering practices. The case study is based on creating a search engine that could benefit requirement engineers, while considering the natural language understanding challenge of technical documents. In order to find a better fit for requirement engineers, we instigate with a traditional search engine and then we augment this traditional search engine to train three different models thus creating additional three neural search engines. We used qualitative analysis to assess the effectiveness of each search engine and understand the user needs. This thesis contributes to the natural language understanding of requirements engineering documentation. Our results indicate that plain text frequency based search engines are sufficient for requirements engineers, however, neural models trained with diverse set of data can improve borderline cases and improve the results altogether. These conclusions are limited to qualitative assessment due to lack of comparative data for quantitative assessment