A Literature Review on Feature Diagram Product Counting and Its Usage in Software Product Line Economic Models

Abstract

In software product line engineering, feature diagrams are a popular means to represent the similarities and differences within a family of related systems. In addition, feature diagrams implicitly model valuable information that can be used in economic models to estimate the cost savings of a product line. In particular, this paper reviews existing proposals on computing the total number of products modeled with a feature diagram and, given a feature, the number of products that implement it. The paper also reviews the economic information that can be estimated when such numbers are known. Thus, this paper contributes by bringing together previously-disparate streams of work: the automated analysis of feature diagrams and economic models for product lines.

A Rule-Learning Approach for Detecting Faults in Highly Configurable Software Systems from Uniform Random Samples

“…In this paper, we have provided a concrete implementation of our approach built on top of a uniform random SAT-sampler and a rule-learning engine. As the input of the random sampler is a propositional logic formula, our prototype supports testing any software whose configurable options are expressed in a language translatable into Boolean logic, such as KConfig [10,11], CDL [44], and Feature Diagrams [45,46].…”

A systematic mapping study of search-based software engineering for software product lines

“…[58,59]) in combination with recent work on SPL economics (e.g. [60,61]), which may help to address the most common development scenario of SPLs whereby multiple existing variants that must be consolidated into a SPL infrastructure to effectively address their maintenance problems [62].…”

Efficient Identification of Core and Dead Features in Variability Models

“…Two arrays named through_high_array and through_low_array are used to store in position i − 1 the values of f i → + 1 and f i + 1, respectively. The computation of such arrays is performed by Algorithm 2 (lines [13][14]. For instance, after executing Algorithm 2 for Figure 2 + 1 = true Then, Algorithm 1 applies Lemmas 1 and 2 to identify the core and dead features according to through_high_array and through_low_array (lines [15][16][17][18][19].…”

“…The usual way to perform that computation is to translate the models into Boolean formulas and use off-the-shelf logic tools, such as SAT solvers [11] or Binary Decision Diagram (BDD) engines [12]. Unfortunately, current algorithms have poor time performance and thus impose long response times, hindering user interactivity [13]. Personal use is also permitted, but republication/redistribution requires IEEE permission.…”