RT Journal Article
T1 Detecting semantic alignments between textual specifications and domain models
A1 Shimangaud, Shwetali
A1 Burgueño-Caballero, Lola
A1 Kienzle, Jörg
A1 Saini, Rijul
K1 Ingeniería del software
K1 Proceso de lenguaje natural (Informática)
K1 Inteligencia artificial
AB Context: Having domain models derived from textual specifications has proven to be very useful in the earlyphases of software engineering. However, creating correct domain models and establishing clear links with thetextual specification is a challenging task, especially for novice modelers.Objective: We propose an approach for determining the alignment between a partial domain model and atextual specification.Methods: To this aim, we use Natural Language Processing techniques to pre-process the text, generate anartificial natural language specification for each model element, and then use an LLM to compare the generateddescription with matched sentences from the original specification. Ultimately, our algorithm classifies eachmodel element as either aligned (i.e., correct), misaligned (i.e., incorrect), or unclassified (i.e., insufficientevidence). Furthermore, it outputs the related sentences from the textual specification that provide the evidencefor the determined class.Results: We have evaluated our approach on a set of examples from the literature containing diverse domains,each consisting of a textual specification and a reference domain model, as well as on models containingmodeling errors that were systematically derived from the correct models through mutation. Our resultsshow that we are able to identify alignments and misalignments with a precision close to 1 and a recallof approximately 78%, with execution times ranging from 18 s to 1 min per model element.Conclusion: Since our algorithm almost never classifies model elements incorrectly, and is able to classifyover 3/4 of the model elements, it could be integrated into a modeling tool to provide positive feedback orgenerate warnings, or employed for offline validation and quality assessment.
PB Elsevier
YR 2026
FD 2026-05-05
LK https://hdl.handle.net/10630/46639
UL https://hdl.handle.net/10630/46639
LA eng
NO Shwetali Shimangaud, Lola Burgueño, Jörg Kienzle, Rijul Saini, Detecting semantic alignments between textual specifications and domain models, Information and Software Technology, Volume 197, 2026, 108154, ISSN 0950-5849, https://doi.org/10.1016/j.infsof.2026.108154.
NO Funding for open access charge: Universidad de Málaga/CBUA
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 24 may 2026