Requirements Quality Assurance in Industry: Why, What and How?

TL;DR

This paper discusses the importance of requirements quality assurance in industry, proposing a taxonomy to characterize verification complexity and automation challenges, aiming to support decision-making in quality assurance processes.

Contribution

It introduces a taxonomy of requirements quality assurance complexity, integrating human cognitive load and automation challenges, to guide automated support development.

Findings

Taxonomy characterizes cognitive and automation complexity.

Framework supports decision-making in quality assurance.

Validation of the taxonomy enables automated support development.

Abstract

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.