Marina Rukavitsyna

dblp:357/9883 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2024
—ORCID · none

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Software engineering, systems software and programming languages · 2 · 2 since 2021
YearPublicationVenuePosition
2024 Benchmarking requirement template systems: comparing appropriateness, usability, and expressiveness
abstract
Abstract 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.
Katharina Großer, Amir Shayan Ahmadian, Marina Rukavitsyna, Qusai Ramadan, Jan Jürjens
Requir. Eng.3
2023 A Comparative Evaluation of Requirement Template Systems
abstract
Context: Multiple semi-formal syntax templates for natural language requirements foster to reduce ambiguity while preserving readability. Yet, existing studies on their effectiveness do not allow to systematically investigate quality benefits and compare different notations. Objectives: We strive for a comparative benchmark and evaluation of template systems to support practitioners in selecting template systems and enable researchers to work on pinpoint improvements and domain-specific adaptions. Methods: We conduct a comparative experiment with a control group of free-text requirements and treatment groups of their variants following different templates. We compare effects on metrics systematically derived from quality guidelines. Results: We present a benchmark consisting of a systematically derived metric suite over seven relevant quality categories and a dataset of 1764 requirements, comprising 249 free-text forms from five projects and variants in five template systems. We evaluate effects in comparison to free text. Except for one template system, all have solely positive effects in all categories. Conclusions: The proposed benchmark enables the identification of the relative strengths and weaknesses of different template systems. Results show that templates can generally improve quality compared to free text. Although MASTER leads the field, there is no conclusive favourite choice, as overall effect sizes are relatively similar.
Katharina Großer, Marina Rukavitsyna, Jan Jürjens
RE2