Requirements quality assurance in industry: Why, what and how?
2017 (English)In: Lect. Notes Comput. Sci., Springer, 2017, 77-84 p.Conference paper (Refereed)
Context and Motivation: Natural language is the most common form to specify requirements in industry. The quality of the specification depends on the capability of the writer to formulate requirements aimed at different stakeholders: they are an expression of the customer’s needs that are used by analysts, designers and testers. Given this central role of requirements as a mean to communicate intention, assuring their quality is essential to reduce misunderstandings that lead to potential waste. Problem: Quality assurance of requirement specifications is largely a manual effort that requires expertise and domain knowledge. However, this demanding cognitive process is also congested by trivial quality issues that should not occur in the first place. Principal ideas: We propose a taxonomy of requirements quality assurance complexity that characterizes cognitive load of verifying a quality aspect from the human perspective, and automation complexity and accuracy from the machine perspective. Contribution: Once this taxonomy is realized and validated, it can serve as the basis for a decision framework of automated requirements quality assurance support.
Place, publisher, year, edition, pages
Springer, 2017. 77-84 p.
Decision support, Natural language processing, Requirements engineering, Requirements quality, Computer software selection and evaluation, Decision support systems, Natural language processing systems, Specifications, Taxonomies, Cognitive process, Decision framework, Decision supports, Domain knowledge, Human perspectives, Natural languages, Requirement specification, Quality assurance
IdentifiersURN: urn:nbn:se:bth-14008DOI: 10.1007/978-3-319-54045-0_6ScopusID: 2-s2.0-85013916456ISBN: 978-3-319-54044-3 (print)OAI: oai:DiVA.org:bth-14008DiVA: diva2:1082310
23rd International Working Conference on Requirements Engineering – Foundation for Software Quality, REFSQ, Essen