FCA for software product line representation: Mixing configuration and feature relationships in a unique canonical representation

Abstract

Software Product Line Engineering (SPLE) is a set of methods to help build a collection of software systems which are similar enough to enable appropriate artefact reuse. An important task consists in documenting in variability models the common and variable features which may compose the similar software systems along with compatibility constraints between these features. Several models and formalisms have been proposed to model variability: each one of them has specific properties making it pertinent to support certain management operations, which are of primary importance in SPLE. Switching from one kind of variability model to another is thus important to benefit from a wide range of operations and efficiently manage a software product line. In this paper, we review the various approaches proposed to manage and organise features and product configurations (a product configuration being a chosen subset of features). We discuss the originality of concept lattices, canonical structures presenting a dual view on features and configurations, and the advantages to use these structures as variability representations. Switching from existing variability models to an equivalent concept lattice raises scaling issues related to the size of the needed input dataset and thus hinders their exploitation. We propose an alternative relying on implicative systems and define a transformation method which does not suffer from scaling issues.

Cyber-Physical Systems, a new formal paradigm to model redundancy and resiliency

“…The proposed result highlights the FCA bottom-up approach focusing with the particularities of the domain and building upon them a structure to allow to discover the general dependencies. Some research studying relations between the FCA and the graph modelling methods (Carbonnel et al, 2016), (Morozov et al, 2017) specifies the need of the use of extra filtering after the lattice building process. As a further development of the approach, we plan to study the use of the resulting lattice-models to response some of the pressing questions of Industry 4.0 such as the identification of redundancies in functionalities, the improvement of the systems plasticity and their auto-adaptation to environment changes.…”

Assisting Configurations-Based Feature Model Composition

“…Concerning product lines inducing a number of configurations not tractable by FCA, our approach also could benefit from product line decomposition: dividing a feature model according to scopes, concerns or teams into less complex interdependent feature models. Besides, the paper [13] gives a procedure to derive (in a polynomial time) an implicative system directly from a feature model, thus without using the configuration set which may be an obstacle in some cases as noticed by [31]. The logical semantics is guaranteed by the FCA framework.…”

Non-redundant implicational base of formal context with constraints using SAT

“…As a method in knowledge discovery, application of formal concept analysis and formal context covers many research domains including computer science and other domains. In computer science, some studies were successful to apply formal concept analysis for solving some problems in many sub-domains, e.g ., datamining ( Aragón, Medina & Ramírez-Poussa, 2022 ; Hao et al, 2023 ), machine learning ( Janostik, Konecny & Krajča, 2022 ), data science ( Bazin et al, 2022 ), intelligent system ( Shao et al, 2023 ), information retrieval ( Ojeda-Hernández, López-Rodríguez & Mora, 2023 ; Khattak et al, 2021 ), natural language processing ( Marín et al, 2021 ; Jain, Seeja & Jindal, 2020 ), decision support system ( Wei et al, 2020 ), recommendation system ( Liu et al, 2022 ), semantic web ( Jindal, Seeja & Jain, 2020 ), cloud computing ( Khemili, Hajlaoui & Omri, 2022 ), data structure ( Ferré & Cellier, 2020 ), mobile application ( Kwon et al, 2021 ), software engineering ( Carbonnel et al, 2020 ), and robotic ( Zhang et al, 2023 ). In addition, some successful studies to apply formal concept analysis were in other domains, e.g ., engineering ( Rocco, Hernandez-Perdomo & Mun, 2020 ), mathematics ( Jäkel & Schmidt, 2022 ; Rocco, Hernandez-Perdomo & Mun, 2020 ), biology ( Gély et al, 2022 ), psychology ( Belohlavek & Mikula, 2022 ), medicine ( Md Saleh, Ab Ghani & Jilani, 2022 ), business ( Wajnberg et al, 2018 ; Ravi, Ravi & Prasad, 2017 ; Acharjya & Das, 2017 ), and social science ( Lang & Yao, 2023 ; Hao et al, 2021 ; Gao et al, 2021 ).…”