Defining categorical reasoning of numerical feature models with feature-wise and variant-wise quality attributes
2022 (English)In: 26th ACM International Systems and Software Product Line Conference, SPLC 2022 - Proceedings, Association for Computing Machinery (ACM) , 2022, p. 132-139Conference paper, Published paper (Refereed)
Abstract [en]
Automatic analysis of variability is an important stage of Software Product Line (SPL) engineering. Incorporating quality information into this stage poses a significant challenge. However, quality-aware automated analysis tools are rare, mainly because in existing solutions variability and quality information are not unified under the same model. In this paper, we make use of the Quality Variability Model (QVM), based on Category Theory (CT), to redefine reasoning operations. We start defining and composing the six most common operations in SPL, but now as quality-based queries, which tend to be unavailable in other approaches. Consequently, QVM supports interactions between variant-wise and feature-wise quality attributes. As a proof of concept, we present, implement and execute the operations as lambda reasoning for CQL IDE - the state-of-the-art CT tool.
Place, publisher, year, edition, pages
Association for Computing Machinery (ACM) , 2022. p. 132-139
Keywords [en]
automated reasoning, category theory, extended feature model, numerical features, quality attribute, Extended feature models, Feature models, Quality attributes, Quality information, Quality variability, Software Product Line, Variability modeling, Quality control
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-328122DOI: 10.1145/3503229.3547057Scopus ID: 2-s2.0-85139107194OAI: oai:DiVA.org:kth-328122DiVA, id: diva2:1762018
Conference
26th ACM International Systems and Software Product Line Conference, ASPLC 2022, 12-16 September 2022
Note
QC 20230602
2023-06-022023-06-022023-06-02Bibliographically approved