Background: Establishing traceability from requirements documents to downstream artifacts early can be beneficial as it allows engineers to reason about requirements quality (e.g. completeness, consistency, redundancy). However, creating such early traces is difficult if downstream artifacts do not exist yet. Objective: We propose to use domain-specific taxonomies to establish early traceability, raising the value and perceived benefits of trace links so that they are also available at later development phases, e.g. in design, testing or maintenance. Method: We developed a recommender system that suggests trace links from requirements to a domain-specific taxonomy based on a series of heuristics. We designed a controlled experiment to compare industry practitioners' efficiency, accuracy, consistency and confidence with and without support from the recommender. Results: We have piloted the experimental material with seven practitioners. The analysis of self-reported confidence suggests that the trace task itself is very challenging as both control and treatment group report low confidence on correctness and completeness. Conclusions: As a pilot, the experiment was successful since it provided initial feedback on the performance of the recommender, insight on the experimental material and illustrated that the collected data can be meaningfully analysed. © 2020 IEEE.

Traditional trace links are established directly between source and target artefacts. This requires that the target artefact exists when the trace is established. We introduce the concept of indirect trace links between a source artefact and a knowledge organization structure, e.g. a taxonomy. This allows the creation of links (we call them taxonomic traces) before target artefacts are created. To gauge the viability of this concept and approach, we developed a prototype, TT-RecS, that allows to create such trace links either manually or with the help of a recommender system. © 2020 IEEE.

One prerequisite for supervised machine learning is high quality labelled data. Acquiring such data is, particularly if expert knowledge is required, costly or even impossible if the task needs to be performed by a single expert. In this paper, we illustrate tool support that we adopted and extended to source domain-specific knowledge from experts. We provide insight in design decisions that aim at motivating experts to dedicate their time at performing the labelling task. We are currently using the approach to identify true synonyms from a list of candidate synonyms. The identification of synonyms is important in scenarios were stakeholders from different companies and background need to collaborate, for example when defining and negotiating requirements. We foresee that the approach of expert-sourcing is applicable to any data labelling task in software engineering. The discussed design decisions and implementation are an initial draft that can be extended, refined and validated with further application. Copyright © 2019 by the paper’s authors.

Domain-specific synonyms occur in many specialized search tasks, such as when searching medical documents, legal documents, and software engineering artifacts. We replicate prior work on ranking domain-specific synonyms in the consumer health domain by applying the approach to a new language and domain: identifying Swedish language synonyms in the building construction domain. We chose this setting because identifying synonyms in this domain is helpful for downstream systems, where different users may query for documents (e.g., engineering requirements) using different terminology. We consider two new features inspired by the change in language and methodological advances since the prior work’s publication. An evaluation using data from the building construction domain supports the finding from the prior work that synonym discovery is best approached as a learning to rank task in which a human editor views ranked synonym candidates in order to construct a domain-specific thesaurus. We additionally find that FastText embeddings alone provide a strong baseline, though they do not perform as well as the strongest learning to rank method. Finally, we analyze the performance of individual features and the differences in the domains. © Springer Nature Switzerland AG 2019.

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.

[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.