From Meaningful Orderings in the Web of Data to Multi-level Pattern Structures

Brabant, Quentin; Couceiro, Miguel; Napoli, Amedeo; Reynaud, Justine

doi:10.1007/978-3-319-60438-1_61

Cited by 2 publications

(3 citation statements)

References 10 publications

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“…Pattern Structures: Ganter and Kuznetsov [GK01] first introduced Pattern Structures for an application on graphs representing molecules. Since, it has been used fo knowledge discovery on complex data types, e.g., mining sequential data [BEJ + 16], RDF triples classification [BCNR17,RATN] or functional dependencies extraction [BKN14]. Relational Concept Analysis: RCA was first used to build class model abstractions [AFHN06].…”

Section: Rq2mentioning

confidence: 99%

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

Carbonnel

Huchard

Nebut

2018

Proceedings of the 12th International Workshop on Variability Modelling of Software-Intensive Systems

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Software product line engineering gathers a set of methods that rely on systematic reuse and mass customisation to reduce the development time and cost of a set of similar software systems. Boolean feature models are the de facto standard used to represent product line variability in terms of features, a feature being a distinguishable characteristic of one or several softwares. The extractive adoption of a product line from a set of individually developed softwares requires to extract variability information from a collection of software descriptions to model their variability. With the appearance of more and more complex software systems, software product line engineering faces new challenges including variability extraction and modelling. Extensions of boolean feature models, as multi-valued attributes or UML-like cardinalities have since been proposed to support variability modelling in complex product lines. In this paper, we propose research directions to address the issue of extracting more complex variability information, as a part of extended feature models synthesis from software descriptions. We consider the capabilities of Formal Concept Analysis, a mathematical framework for knowledge discovery, along with two of its extensions called Pattern Structures and Relational Concept Analysis, to answer this problematic. These frameworks bring theoretical foundations to complex variability extraction algorithms.

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Section: Rq2mentioning

confidence: 99%

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

Carbonnel

Huchard

Nebut

2018

Proceedings of the 12th International Workshop on Variability Modelling of Software-Intensive Systems

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“…Tabular descriptions of existing bot variants have been gathered online. 13 In total, 306 bot variants are described depending on 4 multi-valued attributes (documenting the bot movement and targeting strategies, the fighting type they are designed for, and the license of the source code), and a boolean feature stating if the source code is available. It is a PCM of medium size with incomplete values.…”

Section: Datamentioning

confidence: 99%

“…The authors show how to process the obtained structures to discover relevant information. In the same domain, Barbant et al [13] use Pattern Structures to represent ontologies and discover knowledge in the web of data. Kaytoue et al [35] use pattern structures to mine gene expression data.…”

Section: Related Workmentioning

confidence: 99%

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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From Meaningful Orderings in the Web of Data to Multi-level Pattern Structures

Cited by 2 publications

References 10 publications

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

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

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

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