Machine Learning: A Software Process Reengineering in Software Development Organization
Krunal Bhavsar1, Vrutik Shah2, Samir Gopalan3
1Krunal Bhavsar, Research Scholar, Department of Computer Science & Engineering, Indus University, Ahmedabad, India.
2Dr. Vrutik Shah, Research Guide, Department of Computer Science & Engineering, Indus University, Ahmedabad, India.
3Dr. Samir Gopalan, Research Co-Guide, Business Administration & Management, Indus University, Ahmedabad, India.
Manuscript received on November 22, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 4492-4500 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B4563129219/2019©BEIESP | DOI: 10.35940/ijeat.B4563.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: BPR (Business Process Re-engineering) is an organizational mechanism that improves the organizational ability in responding to the challenges of qualitative result by change management and improvement in software engineering processes, productivity, product quality and competitive advantage. BPR inherits, explores and implements the building of process change, to incorporate enhancements to the essential considerations and protocols of (SEM) Software Engineering Management. Machine Learning (ML) can be the key aspect for BPR in software development organization. The goal of this research study is raising the conceptual vision about integration of automation technology like ML and its life cycle development within Software Development Life Cycle (SDLC) of the software product and highlights benefits and drawbacks ML techniques in SPM (Software Project Management), and how to implement ML in standard SEM practices. We have attempted the introduction of machine learning in SEM to determine specific performance and tasks reuse using empirical analysis and discussion on implementation of ML algorithms. The empirical study of software technologies includes control structure of an autonomous software application. In current era, ML imparts consistently promising accuracy in some SEM fields. The goal of this paper is an empirical and analytical study and literature review to propose desired level of quality software, through the comparative evaluation of existing processes and their respective support for Software Quality Engineering (SQE).
Keywords: AI – Artificial Intelligence, ML – Machine Learning, SEM – Software Engineering Management, BPR – Business Process Reengineering, SE – Software Engineering, BPM – Business Process Management, SPM – Software Project Management.