A Semi-automated Approach to Generate an Adaptive Quality Attribute Relationship Matrix

Abstract

[Context and Motivation] A critical success factor in Requirements Engineering (RE) involves recognizing conflicts in Quality Requirements (QRs). Nowadays, Quality Attributes Relationship Matrix (QARM) is utilized to identify the conflicts in QRs. The static QARM represents how one Quality Attribute (QA) undermines or supports to achieve other QAs. [Question/Problem] However, emerging technology discovers new QAs. Requirements analysts need to invest significant time and non-trivial human effort to acquire knowledge for the newly discovered QAs and influence among them. This process involves searching and analyzing a large set of quality documents from literature and industries. In addition, the use of static QARMs, without knowing the purpose of the QRs in the system may lead to false conflict identification. Rather than taking all QAs, domain-specific QAs are of great concern for the system being developed. [Principal ideas/results] In this paper, we propose an approach which is aimed to build an adaptive QARM semi-automatically. We empirically evaluate the approach and report an analysis of the generated QARM. We achieve 85.67% recall, 59.07% precision and 69.14% F-measure to acquire knowledge for QAs. [Contributions] We provide an algorithm to acquire knowledge for domain-specific QAs and construct an adaptive QARM from available unconstrained natural language documents and web search engines.