TITLE:
Learning to Support Derivation of Adaptable Products in Software Product Lines
AUTHORS:
Anissa Benlarabi, Amal Khtira, Bouchra El Asri
KEYWORDS:
Software Product Lines, Requirement, Similarity, Product Derivation, Natural Language Processing, Semantic Models
JOURNAL NAME:
Journal of Computer and Communications,
Vol.8 No.4,
April
30,
2020
ABSTRACT: Software product line engineering is a large scale development paradigm based on mass production. It consists in building a common platform from which a set of products can be derived. Under the constraints of continuous evolution and costs optimization, the derivation process must be able to answer customers’ requirements and provide adequate products in a short time without defects. However, this purpose cannot be achieved if the customer must wait for the change is implemented in the common platform. In this paper, we describe our work which proposes a framework to manage derivation of adaptable products. An adaptable product is obtained by deriving the most similar product from the common platform and changing it to support the new requirements. The aim of the framework is to give quick response to the customers need while the new requirements are being implemented in the common platform. The framework includes tools for processing natural language requirements, computing the similarity between products on the basis of their requirements, and the product adaptation effort measuring.