Binary Pattern for Nested Cardinality Constraints for Software Product Line of IoT-Based Feature Models

Abstract

Software product line (SPL) is extensively used for reusability of resources in family of products. Feature modeling is an important technique used to manage common and variable features of SPL in applications, such as Internet of Things (IoT). In order to adopt SPL for application development, organizations require information, such as cost, scope, complexity, number of features, total number of products, and combination of features for each product to start the application development. Application development of IoT is varied in different contexts, such as heat sensor indoor and outdoor environment. Variability management of IoT applications enables to find the cost, scope, and complexity. All possible combinations of features make it easy to find the cost of individual application. However, exact number of all possible products and features combination for each product is more valuable information for an organization to adopt product line. In this paper, we have proposed binary pattern for nested cardinality constraints (BPNCC), which is simple and effective approach to calculate the exact number of products with complex relationships between application's feature models. Furthermore, BPNCC approach identifies the feasible features combinations of each IoT application by tracing the constraint relationship from top-to-bottom. BPNCC is an open source and tool-independent approach that does not hide the internal information of selected and non-selected IoT features. The proposed method is validated by implementing it on small and large IoT application feature models with "n'' number of constraints, and it is found that the total number of products and all features combinations in each product without any constraint violation.