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Adaptive AI Pair Programming: Impact on Developer Productivity and Code Quality for Sustainable Industrial Processes
Abstract
Software development has become a critical enabler of sustainable industrial processes, where efficiency, reliability, and adaptability are paramount. Traditional pair programming fosters collaboration and improves code quality, but it is resource-intensive and often constrained by human limitations. The emergence of Adaptive AI Pair Programming, powered by advanced machine learning and large language models, offers a new paradigm that augments developers with real-time assistance, contextual code suggestions, and adaptive learning capabilities. This chapter explores the impact of Adaptive AI Pair Programming on developer productivity and software quality within industrial settings. It examines how AI-powered coding assistants can reduce technical debt, accelerate development cycles, and enhance maintainability, while simultaneously addressing challenges such as over-reliance, bias in AI-generated code, and governance of intellectual property. By linking AI-assisted software engineering with sustainable industrial practices, the chapter highlights the potential of adaptive AI systems to create resilient, energy-efficient, and future-ready digital infrastructures.
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