Towards the Extraction of Variability Information to Assist Variability Modelling of Complex Product Lines

Carbonnel, Jessie; Huchard, Marianne; Nebut, Clémentine

doi:10.1145/3168365.3168378

Cited by 4 publications

(2 citation statements)

References 44 publications

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“…This work is a step towards the global objective of facilitating the transition of complex software variants that have been individually developed, to software reuse and mass-customisation approaches. This paper extends previous work presented in [16] by defining and evaluating the complex variability extraction approach.…”

Section: Introductionsupporting

confidence: 52%

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Towards complex product line variability modelling: Mining relationships from non-boolean descriptions

Carbonnel

Huchard

Nebut

2019

Journal of Systems and Software

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Software product line engineering relies on systematic reuse and mass customisation to reduce the development time and cost of a software system family. The extractive adoption of a product line requires to extract variability information from the description of a collection of existing software systems to model their variability. With the increasing complexity of software systems, software product line engineering faces new challenges including variability extraction and modelling. Extensions of existing boolean variability models, such as multi-valued attributes or UML-like cardinalities, were proposed to enhance their expressiveness and support variability modelling in complex product lines. In this paper, we propose an approach to extract complex variability information, i.e., involving features as well as multi-valued attributes and cardinalities, in the form of logical relationships. This approach is based on Formal Concept Analysis and Pattern Structures, two mathematical frameworks for knowledge discovery that bring theoretical foundations to complex variability extraction algorithms. We present an application on product comparison matrices representing complex descriptions of software system families. We show that our method does not suffer from scalability issues and extracts all pertinent relationships, but that it also extracts numerous accidental relationships that need to be filtered.

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Section: Introductionsupporting

confidence: 52%

“…We have developed a tool in Java called CLEF 16 (for Complex variabiLity Extraction with Fca) to perform all the steps that may be automated to answer these research questions. It is composed of 3 packages:…”

Section: Methodsmentioning

confidence: 99%

Towards complex product line variability modelling: Mining relationships from non-boolean descriptions

Carbonnel

Huchard

Nebut

2019

Journal of Systems and Software

Self Cite

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On-demand Relational Concept Analysis

Bazin

Carbonnel

Huchard

et al. 2019

Formal Concept Analysis

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Formal Concept Analysis and its associated conceptual structures have been used to support exploratory search through conceptual navigation. Relational Concept Analysis (RCA) is an extension of Formal Concept Analysis to process relational datasets. RCA and its multiple interconnected structures represent good candidates to support exploratory search in relational datasets, as they are enabling navigation within a structure as well as between the connected structures. However, building the entire structures does not present an efficient solution to explore a small localised area of the dataset, for instance to retrieve the closest alternatives to a given query. In these cases, generating only a concept and its neighbour concepts at each navigation step appears as a less costly alternative. In this paper, we propose an algorithm to compute a concept and its neighbourhood in extended concept lattices. The concepts are generated directly from the relational context family, and possess both formal and relational attributes. The algorithm takes into account two RCA scaling operators. We illustrate it on an example.

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Facilitating Reuse of Functions in Embedded Software

2022

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Developing software-intensive embedded systems is a significant challenge as embedded systems have become more complex and dynamic for integrating various constraints (e.g., real-time performance, design, and functional safety). In general, software practitioners reuse code to reduce development time for implementing required functionalities. However, embedded software development still lacks code reuse on a large scale due to its intricate constraints. This paper presents an approach to identifying reusable functions and their relationships from the legacy embedded software for faster development reducing functionality testing time. A python tool is developed to automatically extract the reusable functions and present them in a feature model. The search time for finding reusable functions speeds up when they are placed as features in a feature model. We conducted experiments on three GitHub embedded software projects (elevator, infotainment, and health monitoring systems). The experimental results find that our proposed approach reduces the feature search time by 59.0%, 49.8%, and 74.5%, respectively, compared to the manual searching approach for 50 features.

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Towards the Extraction of Variability Information to Assist Variability Modelling of Complex Product Lines

Cited by 4 publications

References 44 publications

Towards complex product line variability modelling: Mining relationships from non-boolean descriptions

Towards complex product line variability modelling: Mining relationships from non-boolean descriptions

On-demand Relational Concept Analysis

Facilitating Reuse of Functions in Embedded Software

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