Framework for the Automation of SDLC Phases using Artificial Intelligence and Machine Learning Techniques

Abstract

Software Engineering acts as a foundation stone for any software that is being built. It provides a common road-map for construction of software from any domain. Not following a well-defined Software Development Model have led to the failure of many software projects in the past. Agile is the Software Development Life Cycle (SDLC) Model that is widely used in practice in the IT industries to develop software on various technologies such as Big Data, Machine Learning, Artificial Intelligence, Deep learning. The focus on Software Engineering side in the recent years has been on trying to automate the various phases of SDLC namely- Requirements Analysis, Design, Coding, Testing and Operations and Maintenance. Incorporating latest trending technologies such as Machine Learning and Artificial Intelligence into various phases of SDLC, could facilitate for better execution of each of these phases. This in turn helps to cut-down costs, save time, improve the efficiency and reduce the manual effort required for each of these phases. The aim of this paper is to present a framework for the application of various Artificial Intelligence and Machine Learning techniques in the different phases of SDLC.

The Use of AI in Software Engineering: Synthetic Knowledge Synthesis of Recent Research Literature

“…Fuzzy logic techniques have been used in selecting software requirements from elicited software requirements or ordering them by preferences [23], cloud-based testing adaption [24], and software effort estimation [25] Automation of software testing in an agile environment. Artificial intelligence has been used to generate test cases for automatic testing in agile environments [26][27][28] and to automate other phases of the software development lifecycle [29].…”

The Use of AI in Software Engineering: A Synthetic Knowledge Synthesis of the Recent Research Literature

“…Fuzzy logic techniques have been used in selecting software requirements from the elicited software requirements or ordering them by preferences [26], cloud-based testing adaption [27], and software effort estimation [28] Automation of software testing in an agile environment Artificial intelligence has been used to generate test cases for automatic testing in agile environments [29][30][31] and to automate other phases of the software development lifecycle [32].…”

“…Fuzzy logic techniques have been used to support project management activities like cost and effort estimation [30,31], imputation of missing values in empirical software project management [32], management of outsourcing [33], risk assessment in the agile environment [34] and software product promotion [33].…”