Developer productivity in AI-driven engineering teams
Software Architect, San Jose, CA, USA.
Research Article
World Journal of Advanced Research and Reviews, 2023, 18(02), 1489-1502
Publication history:
Received on 10 April 2023; revised on 11 May 2023; accepted on 12 May 2023
Abstract:
Artificial Intelligence (AI) is transforming the software engineering practice of the modern days, and it directly affects how development teams are organized and how the productivity of the developer is gauged. Artificially intelligent applications are becoming common place, in the form of code completion tools or assistants, automated testing suites, and intelligent project management systems, to automate workflows, lessen human effort and enable decision-making on the basis of data. This paper discusses how AI may affect the productivity of developers working in a software engineering team and what the opportunities and limitations of AI application in the real-life context are. The article examines the ways of applying AI to a core activity, such as code generation, bug detection, software testing and agile project coordination using a systematic review of recent empirical studies supported by industry case examples. The results also suggest that AI-enabled devices can be used to provide about 25-35 percent of efficiency in overall development, which is mainly facilitated by around 30 percent faster routine code writing and approximately 20 percent less time to debug code. Besides efficiency benefits, AI can improve teamwork, particularly in agile and distributed teams, through creating a common view of work progress and automatic information about possible risks, as well as bottlenecks. Nonetheless, the paper also reports major issues, such as the learning curve of new tools, constant maintenance overhead, possibility of over-reliance on automated recommendations, and possible denial of critical thinking and craftsmanship among developers. It is concluded in the paper that AI could significantly enhance productivity of developers, yet sustainable gains would be achieved under planned intake of integration measures, sustained training, transparent governance, and moderate association of AI support with human judgment and experience.
Keywords:
AI-driven software engineering; Developer productivity; Machine learning in development; Agile project management; Software engineering innovations; AI tools in engineering
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Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0