[Context:] The quality of requirements engineeringartifacts, e.g. requirements specifications, is acknowledged tobe an important success factor for projects. Therefore, manycompanies spend significant amounts of money to control thequality of their RE artifacts. To reduce spending and improvethe RE artifact quality, methods were proposed that combinemanual quality control, i.e. reviews, with automated approaches.[Problem:] So far, we have seen various approaches to auto-matically detect certain aspects in RE artifacts. However, westill lack an overview what can and cannot be automaticallydetected. [Approach:] Starting from an industry guideline forRE artifacts, we classify 166 existing rules for RE artifacts alongvarious categories to discuss the share and the characteristics ofthose rules that can be automated. For those rules, that cannotbe automated, we discuss the main reasons. [Contribution:] Weestimate that 53% of the 166 rules can be checked automaticallyeither perfectly or with a good heuristic. Most rules need onlysimple techniques for checking. The main reason why some rulesresist automation is due to imprecise definition. [Impact:] Bygiving first estimates and analyses of automatically detectable andnot automatically detectable rule violations, we aim to provide anoverview of the potential of automated methods in requirementsquality control.

While in every organization corporate culture and history change over time, intentional efforts to identifyperformance problems are of particular interest when trying to understand the current state of an organization.The results of past improvement initiatives can shed light on the evolution of an organization, and represent,with the advantage of perfect hindsight, a learning opportunity for future process improvements. Weencountered the opportunity to test this premise in an applied research collaboration with the SwedishTransport Administration (STA), the government agency responsible for the planning, implementation andmaintenance of long-term rail, road, shipping and aviation infrastructure in Sweden.

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.