Causal relations in natural language (NL) requirements convey strong, semantic information. Automatically extracting such causal information enables multiple use cases, such as test case generation, but it also requires to reliably detect causal relations in the first place. Currently, this is still a cumbersome task as causality in NL requirements is still barely understood and, thus, barely detectable. In our empirically informed research, we aim at better understanding the notion of causality and supporting the automatic extraction of causal relations in NL requirements. In a first case study, we investigate 14.983 sentences from 53 requirements documents to understand the extent and form in which causality occurs. Second, we present and evaluate a tool-supported approach, called CiRA, for causality detection. We conclude with a second case study where we demonstrate the applicability of our tool and investigate the impact of causality on NL requirements. The first case study shows that causality constitutes around 28 % of all NL requirements sentences. We then demonstrate that our detection tool achieves a macro-F 1 score of 82 % on real-world data and that it outperforms related approaches with an average gain of 11.06 % in macro-Recall and 11.43 % in macro-Precision. Finally, our second case study corroborates the positive correlations of causality with features of NL requirements. The results strengthen our confidence in the eligibility of causal relations for downstream reuse, while our tool and publicly available data constitute a first step in the ongoing endeavors of utilizing causality in RE and beyond. © 2022, The Author(s).

Context

Advances in technical debt research demonstrate the benefits of applying the financial debt metaphor to support decision-making in software development activities. Although decision-making during requirements engineering has significant consequences, the debt metaphor in requirements engineering is inadequately explored.

Objective

We aim to conceptualize how the debt metaphor applies to requirements engineering by organizing concepts related to practitioners’ understanding and managing of requirements engineering debt (RED).

Method

We conducted two in-depth expert interviews to identify key requirements engineering debt concepts and construct a survey instrument. We surveyed 69 practitioners worldwide regarding their perception of the concepts and developed an initial analytical theory.

Results

We propose a RED theory that aligns key concepts from technical debt research but emphasizes the specific nature of requirements engineering. In particular, the theory consists of 23 falsifiable propositions derived from the literature, the interviews, and survey results.

Conclusions

The concepts of requirements engineering debt are perceived to be similar to their technical debt counterpart. Nevertheless, measuring and tracking requirements engineering debt are immature in practice. Our proposed theory serves as the first guide toward further research in this area.

Quality factors like passive voice or sentence length are commonly used in research and practice to evaluate the quality of natural language requirements since they indicate defects in requirements artifacts that potentially propagate to later stages in the development life cycle. However, as a research community, we still lack a holistic perspective on quality factors. This inhibits not only a comprehensive understanding of the existing body of knowledge but also the effective use and evolution of these factors. To this end, we propose an ontology of quality factors for textual requirements, which includes (1) a structure framing quality factors and related elements and (2) a central repository and web interface making these factors publicly accessible and usable. We contribute the first version of both by applying a rigorous ontology development method to 105 eligible primary studies and construct a first version of the repository and interface. We illustrate the usability of the ontology and invite fellow researchers to a joint community effort to complete and maintain this knowledge repository. We envision our ontology to reflect the community's harmonized perception of requirements quality factors, guide reporting of new quality factors, and provide central access to the current body of knowledge.

High-quality requirements minimize the risk of propagating defects to later stages of the software development life-cycle. Achieving a sufficient level of quality is a major goal of requirements engineering. This requires a clear definition and understanding of requirements quality. Though recent publications make an effort at disentangling the complex concept of quality, the requirements quality research community lacks identity and clear structure which guides advances and puts new findings into an holistic perspective. In this research commentary we contribute(1) a harmonized requirements quality theory organizing its core concepts, (2) an evaluation of the current state of requirements quality research, and (3) a research roadmap to guide advancements in the field.