Uncovering Quality-attribute Concerns in Use-case Specifications via Early Aspect Mining

Abstract

Quality-attribute requirements describe constraints on the development and behavior of a software system, and their satisfaction is key for the success of a software project. Detecting and analyzing quality attributes in early development stages provides insights for system design, reduces risks, and ultimately improves the developers? understanding of the system. A common problem, however, is that quality-attribute information tends to be understated in requirements specifications and scattered across several documents. Thus, making the quality attributes first-class citizens becomes usually a time-consuming task for analysts. Recent developments have made it possible to mine concerns semi-automatically from textual documents. Leveraging on these ideas, we present a semiautomated approach to identify latent quality attributes that works in two stages. First, a mining tool extracts early aspects from use cases, and then these aspects are processed to derive candidate quality attributes. This derivation is based on an ontology of quality-attribute scenarios. We have built a prototype tool called QAMiner to implement our approach. The evaluation of this tool in two case studies from the literature has shown interesting results. As main contribution, we argue that our approach can help analysts to skim requirements documents and quickly produce a list of potential quality attributes for the system.