Towards variability process model for agile product line configuration engineering

Integration of Software Product Line (SPL) and Agile Software Development (ASD) results in a new direction called Agile Product Line Engineering (APLE). Even though some studies in the literature have suggested efficient methods for integrating ASD and SPL, they have not yet addressed every facet of APLE's characteristics, and these methods hardly ever take into account the SPL configuration process or the reuse of software resources when developing new products or expanding existing product lines. Despite extensive research efforts, a unified and holistic APLE methodology that integrates agile concepts across both Application Engineering (AE) and Domain Engineering (DE) phases remains elusive. Given this, we suggest a new APLE methodology to integrate ASD and SPL more effectively. The suggested approach iteratively builds the product line, and the system architecture grows over time. We have outlined a new variability mechanism called Variability on DemAnd (VODA) to boost the SPL configuration process. We performed the two-phased evaluation. (1) In the first phase, we considered empirical investigation to validate the proposed APLE methodology. We performed a randomized experiment to compare the proposed approach to a traditional system that typically applies agile principles within a proactive SPL but lacks agile-based variability mechanisms, dynamic product line architectures, and robust feedback. (2) In second phase, the proposed algorithm is tested for efficiency, performance, and effectiveness. We conduct the experiments to evaluate the proposed process (VODA) and obtained results are evaluated with Precision, Recall, Accuracy, and F-Measure. The findings indicate that the suggested approach offers benefits such as adaptable demand management, improved software resource reuse, lower configuration costs, and a shorter time to market. The second step (i.e. second phase of evaluation) results confirm the effectiveness of the proposed algorithm. The average precision value is 0.887, the average Recall value is 0.884 and the average F-Measure value is 0.878%.