Automated extraction and visualization of quality concerns from requirements specifications

Abstract

Software requirements specifications often focus on functionality and fail to adequately capture quality concerns such as security, performance, and usability. In many projects, quality-related requirements are either entirely lacking from the specification or intermingled with functional concerns. This makes it difficult for stakeholders to fully understand the quality concerns of the system and to evaluate their scope of impact. In this paper we present a data mining approach for automating the extraction and subsequent modeling of quality concerns from requirements, feature requests, and online forums. We extend our prior work in mining quality concerns from textual documents and apply a sequence of machine learning steps to detect quality-related requirements, generate goal graphs contextualized by project-level information, and ultimately to visualize the results. We illustrate and evaluate our approach against two industrial health-care related systems.