Extreme Programming for Execution of Large and Geographical Distributed Software Projects

Abstract

There have been a number of software development processes. Based on their advantages and disadvantages, they are accepted and utilized. But none of the development process can be claimed to be suitable to all the projects as the development context/scenarios differ. Therefore, there is a need to model the software development process based on the on-going situations. In this paper, we define and develop a software development process that uses Micro Service Architecture and DevOp culture for Extreme Programming (XP). The proposed software development process can be used for large, complex and geographically distributed software system. A literature review was conducted to understand the currently used software development process and architectures in extreme programming. Nevertheless, in practice, the usage of extreme programming in large scale companies/systems is not known. This study aims to evaluate the impact of the usage of XP process on the development of large-scale distributed systems, while taking online shopping services as a case study. A case study is conducted in an organization that develops software that are large-size and complex by modifying the extreme programming. Questionnaires were prepared and qualitative analysis was carried out. The case study aided in learning about the effectiveness of combining XP with DevOp. Further, outcomes of Crystal-Clear Methodology that depends on people rather than processes and Extreme Programming were compared. As a result of this process, XP can handle large, complex and geographical distributed software systems. Developed software becomes much faster, cost effective, loosely coupled, deployable across the globe. It is observed that practices of Extreme Programming when adapted in the project gives rise to output like people factor and also helps creating ideas and solutions for complex design issues. This is the approach for conceptualization and implementation of overall systems.