Corresponding author: Venilton FalvoJr ( falvojr@usp.br ) © Venilton FalvoJr, Anderson da Silva Marcolino, Nemésio Freitas Duarte Filho, Edson OliveiraJr, Ellen Francine Barbosa. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY-ND 4.0). This license allows reusers to copy and distribute the material in any medium or format in unadapted form only, and only so long as attribution is given to the creator. The license allows for commercial use. Citation:
FalvoJr V, Marcolino AS, Duarte Filho NF, OliveiraJr E, Barbosa EF (2022) Development and Evaluation of a Software Product Line for M-Learning Applications. JUCS - Journal of Universal Computer Science 28(10): 1058-1086. https://doi.org/10.3897/jucs.90663 |
The popularity of mobile devices in all social classes has motivated the development of mobile learning (m-learning) applications. The existing applications, even having many benefits and facilities in relation to the teaching-learning process, still presents problems and challenges, es- pecially regarding the development, reuse and architectural standardization. Particularly, there is a growing adoption of the Software Product Line (SPL) concept, in view of research that investigates these gaps. This paradigm enables organizations to explore the similarities and variabilities of their products, increasing the reuse of artifacts and, consequently, reducing costs and development time. In this context, we discuss how systematic reuse can improve the development of solutions in the m-learning domain. Therefore, this work presents the design, development and experimental evaluation of M-SPLearning, an SPL created to enable the systematic production of m-learning applications. Specifically, the conception of M-SPLearning covers from the initial study for an effective domain analysis to the implementation and evaluation of its functional version. In this regard, the products have been experimentally evaluated by industry software developers, pro- viding statistical evidence that the use of our SPL can speed up the time-to-market of m-learning applications, in addition to reducing their respective number of faults.