A Review of Frameworks, and Impact on the Software Development Life Cycle with Artificial Intelligence in Software Engineering

Authors

  • Sridhar Reddy Bandaru Program Management, IT, University or Client: Microsoft, Role: Senior ACE Engineer, State and Country: Redmond, WA. Author
  • Dhuli Shyam Business Application, IT, University or Client: Nagase Holdings America Corp, Role: Manager, Application & Software Development, State and Country: NYC, NY. Author
  • Prabu Manoharan Information Technology, University or Client: Bourns Inc, Role: HRIS Manager, State and Country: California, USA. Author
  • Muzaffer Hussain Syed Sr Software Developer, Visual Technologies, Plano, TX. Author
  • Uday Kumar Ragireddy Sr Technical Program Manager, Vdrive IT Solutions, Inc, Richardson, Texas. Author
  • Prasanth Varma Addepalli Data Engineer II, Cox Automotive Corp Svcs LLC, Atlanta, Georgia. Author

DOI:

https://doi.org/10.63282/3117-5481/AIJCST-V4I1P105

Keywords:

Software Engineering, Artificial Intelligence, AI Frameworks, SDLC, Machine Learning, Agent-Based Systems, Knowledge-Based Systems, Automation

Abstract

Advances in intelligent software engineering (ISE) have unfolded in the last years. The development of intelligence software engineering as a new field that exemplifies collaboration between artificial intelligence (AI) and software engineering (SE) is one of these advancements.  The integration of AI capabilities into the Software Development Life Cycle (SDLC) across all phases through machine learning applications and frameworks, adaptive learning, and data-driven decision-making techniques is a recent trend in contemporary software engineering.  With reference to the fundamental frameworks of machine learning, agent-based, and knowledge-based systems that enable efficient software planning, development, testing, and maintenance, the article delivers a thorough overview of application of AI in SE. This paper addresses way AI improves requirement analysis, shortens design and coding time, boosts defect prediction and reinforces testing and maintenance processes with AI tools and automated processes. Also, the paper discusses challenges in implementing AI-based solutions, including data quality concerns, integration risks, model explainability, ethical risks, and high computational costs. A deep analysis of existing literature also shows the growing synergy of AI studies and software engineering practice. Combining structures, effects, and constraints, this paper provides insightful critique on the changing relationship between AI and SE and on the way forward in forthcoming developments.

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Published

2022-01-17

Issue

Vol. 4 No. 1 (2022)

Section

Articles

How to Cite

[1]
S. R. Bandaru, D. Shyam, P. Manoharan, M. H. Syed, U. K. Ragireddy, and P. V. Addepalli, “A Review of Frameworks, and Impact on the Software Development Life Cycle with Artificial Intelligence in Software Engineering”, AIJCST, vol. 4, no. 1, pp. 44–53, Jan. 2022, doi: 10.63282/3117-5481/AIJCST-V4I1P105.

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