Software product line engineering is a method of producing a set of related products that share more commonalities than variability in a cost-effective approach. Software product lines provide systematic reuse within a product family. Extended feature models with cardinalities are widely used for managing variability and commonality in the software product line domains. In this paper, we use promotion technique in Alloy to formalize constraint based extended feature models with cardinalities and their specialization and generalization. This technique has a significant influence on applying analysis operations on feature models. To show the benefits of the promotion technique, we calculate the reuse ratio of a feature in a large scale software product line. In the presented method, in addition to feature and group cardinalities, we consider different combinations of cardinalities with each other as well as feature cloning.

Recommender systems (RSs) are a significant subclass of the information filtering system. RSs seek to predict the rating or preference that a user would give to an item in various online application community fields. Collaborative filtering (CF) is a technique which predicts user distinctions by learning past user-item relationships. However, it is hard to perceive the comparable interests between customers in light of the fact that the sparsity problem is caused by the deficient number of the relationship between users. It is a challenge which limited the ease of use of CF. This paper proposes a novel fuzzy C-means clustering approach which is used to deal with this sparsity problem by utilising a sparsest sub-graph detection algorithm in defining initial centres of the clustering method. The approach uses adaptability of fuzzy logic to make better personalised recommendations in terms of precision, recall and F-measure. The authors present a case study where GitHub is used to show the effectiveness of authors' approach. Authors' model can recommend relevant human resources (HR) to project leaders who have participated in similar projects. The comparative experiment results show that the planned approach will effectively solve the sparseness drawback and produce suitable coverage rate and recommendation quality.

Summary Although the importance of models continuously grows in software development, common development approaches are less able to integrate the automatic management of model integrity into the development process. These critically important constraints may ensure the coherence of models in the evolution process to prevent manipulations that could violate defined constraints on a model. This paper proposes an integrity framework in the context of model‐driven architecture to achieve sufficient structural code coverage at a higher program representation level than machine code. Our framework offers to propagate the modifications from a platform‐independent specification to the corresponding test template model while keeping the consistency and integrity constraints after system evolution. To examine the efficiency of the proposed framework, a quantitative analysis plan is evaluated based on two experimental case studies. In addition, we propose coverage criteria for integrity regression testing (IRT), derived from logic coverage criteria that apply different conceptual levels of testing for the formulation of integrity requirements. The defined criteria for IRT reduce the inherent complexity and cost of verifying complex design changes in regression testing while keeping the fault detection capability with respect to the changes. The framework aims to keep pace with IRT in a formal way. The framework can solve a number of restricted outlooks in model integrity and some limiting factors of incremental maintenance and retesting. The framework satisfies several valuable quality attributes in software testing, such as safety percentage, precision, abstract fault detection performance measurable coverage level, and generality.

Software Product Lines (SPLs) are one of the ways to develop software products by increasing productivity and reducing cost and time in the software development process. In SPLs, each product has many features and it is necessary to consider the optimal and custom features of the products. In fact, selecting key features in SPLs is a challenging process. Feature selection in SPLs is an optimization problem and an NP-Hard problem. One way to select a feature is to use meta-heuristic algorithms modeled on nature, i.e., Bat Algorithm. By modeling bat behavior in prey hunting, a suitable meta-innovative algorithm is considered. This algorithm has important advantages that make it more accurate than conventional methods such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm. In this paper, to select software product features, idol binary algorithm and artificial neural network are used to identify important features of software products that reduce production costs and time. The experiments in MATLAB software and datasets related to software production lines show that the rate of reduction of target performance error or feature selection cost in software production lines in the proposed method has decreased by 64.17% with increasing population.

No abstract