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

Abstract

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.

Exploring Variability of Visual Accessibility Options in Operating Systems

“…The lattices show a reasonable classification of systems, e.g., systems of the same family provide close or similar features. The variability relationships number is not surprising, with regard to previous evaluations we did [36]. There are likely many accidental relationships.…”

“…Table 6 presents the number of features, as well as the exact and the upper bound of binary implications, co-occurrences, and mutex number. The upper bounds are computed using the formulas in [36]. We notice that the Brightness mechanism has the smallest values; the Contrast mechanism has the average values; the Text style mechanism, the Zoom mechanism and the central lattice have the largest values.…”

“…is the feature number) [36]. • Binary implications correspond to the case where when a first feature is present, a second one is mandatorily present too.…”

“…• Binary implications correspond to the case where when a first feature is present, a second one is mandatorily present too. A method to extract binary implications is described in [36]. The method uses the set of all pairs (C i , C j ) ∈ AC K × AC K such that C i ≤ s C j .…”

“…An example of binary implication relation is ReduceTransparency → NormalContrast because Concept_Contrast_3≤ s Concept_ Contrast_5 (see Figure 7b). In a concept lattice, there are at most | A | 2 binary implications (where A is the feature number) [36]. • Mutex are pairs of mutually exclusive features {a 1 ,a 2 } which satisfy a 1 → ¬a 2 (or equivalently a 2 → ¬a 1 ).…”