Survey and Comparative Study on Agile Methods in Software Engineering

Authors

  • Achrak El Mehdi LIROSA Lab National School of Applied Sciences TETOUAN, Morocco
  • Chkouri Mohamed Yassin SIGL Research Team National School of Applied Sciences TETOUAN, Morocco
  • El Kadiri Kamal Eddine LIROSA Lab National School of Applied Sciences TETOUAN, Morocco

DOI:

https://doi.org/10.14738/tmlai.54.3203

Keywords:

Agile Methods, Development Process, Information Systems, Software Engineering.

Abstract

 Today‘s business environment is very much dynamic, and organisations are constantly changing their software requirements to adjust with new environment. They also demand for fast delivery of software products as well as for accepting changing requirements. In this aspect, traditional plan-driven developments fail to meet up these requirements. Though traditional software development methodologies, such as life cycle-based structured and object oriented approaches, continue to dominate the systems development few decades and much research has done in traditional methodologies, Agile software development brings its own set of novel challenges that must be addressed to satisfy the customer through early and continuous delivery of the valuable software.

This article is located in the field of engineering Information Systems (IS) and in particular in the world of engineering process. In order, to produce information systems that the customer needs, many methods engineering were introduced. The first methods introduced are called sequential methods like Cascade method, V cycle or the Y cycle then there are iterative methods like RUP or Symphony and finally agile methods like Scrum and XP  have emerged.


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Published

2017-09-01

How to Cite

El Mehdi, A., Yassin, C. M., & Kamal Eddine, E. K. (2017). Survey and Comparative Study on Agile Methods in Software Engineering. Transactions on Machine Learning and Artificial Intelligence, 5(4). https://doi.org/10.14738/tmlai.54.3203

Issue

Section

Special Issue : 1st International Conference on Affective computing, Machine Learning and Intelligent Systems