Requirements quality research: a harmonized theory, evaluation, and roadmap

Various semi-formal syntax templates for natural language requirements foster to reduce ambiguity while preserving human readability. Existing studies on their effectiveness focus on individual notations only and do not allow to systematically investigate quality benefits. We strive for a comparative benchmark and evaluation of template systems to assist practitioners in selecting appropriate ones and enable researchers to work on pinpoint improvements and domain-specific adaptions. We conduct comparative experiments with five popular template systems—EARS, Adv-EARS, Boilerplates, MASTeR, and SPIDER. First, we compare a control group of free-text requirements and treatment groups of their variants following the different templates. Second, we compare MASTeR and EARS in user experiments for reading and writing. Third, we analyse all five meta-models’ formality and ontological expressiveness based on the Bunge-Wand-Weber reference ontology. The comparison of the requirement phrasings across seven relevant quality characteristics and a dataset of 1764 requirements indicates that, except SPIDER, all template systems have positive effects on all characteristics. In a user experiment with 43 participants, mostly students, we learned that templates are a method that requires substantial prior training and that profound domain knowledge and experience is necessary to understand and write requirements in general. The evaluation of templates systems’ meta-models suggests different levels of formality, modularity, and expressiveness. MASTeR and Boilerplates provide high numbers of variants to express requirements and achieve the best results with respect to completeness. Templates can generally improve various quality factors compared to free text. Although MASTeR leads the field, there is no conclusive favourite choice, as most effect sizes are relatively similar.

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Applying bayesian data analysis for causal inference about requirements quality: a controlled experiment

Frattini,

Fucci,

Torkar

et al. 2024

Empir Software Eng

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It is commonly accepted that the quality of requirements specifications impacts subsequent software engineering activities. However, we still lack empirical evidence to support organizations in deciding whether their requirements are good enough or impede subsequent activities. We aim to contribute empirical evidence to the effect that requirements quality defects have on a software engineering activity that depends on this requirement. We conduct a controlled experiment in which 25 participants from industry and university generate domain models from four natural language requirements containing different quality defects. We evaluate the resulting models using both frequentist and Bayesian data analysis. Contrary to our expectations, our results show that the use of passive voice only has a minor impact on the resulting domain models. The use of ambiguous pronouns, however, shows a strong effect on various properties of the resulting domain models. Most notably, ambiguous pronouns lead to incorrect associations in domain models. Despite being equally advised against by literature and frequentist methods, the Bayesian data analysis shows that the two investigated quality defects have vastly different impacts on software engineering activities and, hence, deserve different levels of attention. Our employed method can be further utilized by researchers to improve reliable, detailed empirical evidence on requirements quality.

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Requirements quality research: a harmonized theory, evaluation, and roadmap

Cited by 3 publications

References 126 publications

Identifying Relevant Factors of Requirements Quality: An Industrial Case Study

Identifying Relevant Factors of Requirements Quality: An Industrial Case Study

Benchmarking requirement template systems: comparing appropriateness, usability, and expressiveness

Applying bayesian data analysis for causal inference about requirements quality: a controlled experiment

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