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Intelligent Software Development Approaches for Emerging Technologies

Author(s) Waweru Mwangi
Country Kenya
Abstract The rapid emergence of artificial intelligence, Internet of Things, edge computing, cloud-native platforms, blockchain, extended reality, autonomous systems, and advanced data technologies is fundamentally transforming software development practices. Conventional software engineering approaches remain important, but the increasing complexity, scale, connectivity, and adaptability requirements of emerging technologies demand more intelligent and flexible development strategies. Intelligent software development incorporates artificial intelligence, machine learning, automated reasoning, predictive analytics, intelligent testing, automated code analysis, continuous monitoring, and data-driven decision support across the software development lifecycle.
This study examines intelligent software development approaches for emerging technologies and proposes an integrated development framework capable of supporting requirements engineering, architecture design, coding, testing, deployment, monitoring, and continuous improvement. A conceptual and descriptive research design was adopted through thematic analysis of scholarly literature related to intelligent software engineering, AI-assisted programming, automated software testing, DevOps, cloud-native development, Internet of Things applications, edge computing, autonomous systems, and adaptive software architectures.
The analysis indicates that intelligent development approaches can improve development productivity, software quality, defect identification, testing efficiency, maintainability, scalability, and adaptability. Artificial intelligence can support requirements analysis and generate development recommendations, while machine learning can assist with defect prediction and risk assessment. Automated testing can prioritize high-risk scenarios, and intelligent monitoring can identify performance anomalies after deployment. Emerging technology environments additionally require software architectures capable of handling distributed computation, dynamic workloads, heterogeneous devices, real-time information, and rapidly changing user requirements.
The proposed framework therefore combines intelligent requirements engineering, adaptive architecture, AI-assisted development, predictive quality management, intelligent testing, automated deployment, and continuous operational feedback. The study also identifies challenges involving unreliable training data, explainability, cybersecurity, model drift, interoperability, legacy integration, computational costs, developer acceptance, and ethical responsibility. The findings suggest that intelligent software development should not be understood merely as the automation of programming activities but as a broader transformation of the software lifecycle into a data-driven, adaptive, predictive, and continuously learning process. The study concludes that organizations adopting intelligent software development approaches can improve their capacity to develop reliable and scalable solutions for emerging technologies, provided that intelligent tools remain subject to human oversight, rigorous validation, security controls, and established software engineering principles.
Keywords Intelligent Software Development, Emerging Technologies, Artificial Intelligence, Machine Learning, Software Engineering, Automated Testing, DevOps, Cloud Computing, Internet of Things, Adaptive Software.
Field Engineering
Published In Volume 2, Issue 5, September-October 2020
Published On 2020-10-31
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