Boosting Developer Efficiency with AI: A Technical Review
Year: 2025
Type: article
Abstract: AI has emerged as a disruptive technology in modern software development and has improved traditional development approaches through intelligent automation and machine learning. This technical review outlines the full integration of AI technology into an entire software development lifecycle, along with how emergent algorithms and neural architectures provide means to increase developer productivity and code quality by using automation. AI-based tooling is being used for critical activities: automated coding, intelligent debugging and remediation, intelligent code review, documentation systems, and natural language processing for stakeholder communication. Today's AI systems have become adept at understanding programming semantics, producing language-matched code snippets, and recognizing exploitable patterns to create vulnerabilities. Optimizing project management, including parts management, scheduling meetings, and forecasts, can include the merits of productive analytic options based on deep learning patterns derived from massive datasets of historical data to guide decision-making and project resource management. The automated testing space can characterize the frenzy of test scenarios a tester must generate, while developing modern frameworks can engross themselves in extracting other tests from interacting sources by keeping track of various codes continually modified. Here, the converging elements and productivity boosts associated with AI introduce an elevating environment that accommodates human creative thought and productivity while automating the mundane. Moreover, will explore a new academic model of work, defining collaborative relationships that can ensure an effective future of human workers and AI adapting the conventions of modern software engineering.
Author: Siddhant Sonkar
Institution: University of California, Irvine
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7083497623
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