Large-scale Software Product Line Research Articles

An approach for optimized feature selection in large-scale software product lines

Context: Feature selection in product line engineering is an essential step for individual product customization, in which the multiple objectives, that are often competing and conflicting, have to be taken into consideration. These objectives always need to be balanced during selection, leading to a process of multi-objective optimization. What’s more, the massive complex dependency and constraint relationships between features present another huge challenge for optimization. Objective: In this work, we propose a multi-objective optimization algorithm, IVEA-II, to automatically search through configurations to obtain an optimal balance between various objectives. Additionally, all the relationships between features must be conformed to by the optimal feature solutions. Method: Firstly, a two-dimensional fitness function in our previous work is reserved. Secondly, to prevent the negative impact of this 2D fitness on the diversity of final Pareto Fronts, the crowding distance is introduced into each fitness-based selection. Lastly, a new mutation operator is designed to improve the scalability of IVEA-II. Results: A series of experiments were conducted to verify the effectiveness of IVEA-II on five large-scale feature models with five optimization goals. Conclusion: Experiments showed that IVEA-II can generate more valid solutions over a set period of time, with optimal solutions also having better diversity and convergence.

Automatic promotional specialization, generalization and analysis of extended feature models with cardinalities in Alloy

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.