Artificial Intelligence: Augmented Integrated Development Environments for Boosting Programmer Productivity

Ashok, P. and Gorli, Ravi and Parameswari, S. and Sridevi, Lakshmi and Janaki, N. and Gopinath, S. and Anandaram, Harishchander and Shreenidhi, K. S. and Iyengar, Samaya Pillai (2025) Artificial Intelligence: Augmented Integrated Development Environments for Boosting Programmer Productivity. In: Artificial Intelligence for Cloud-Native Software Engineering. IGI Global Scientific Publishing, pp. 29-56.

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Abstract

P. Ashok Symbiosis Institute of Digital and Telecom Management, India https://orcid.org/0000-0002-5859-6041 Ravi Gorli GITAM University, India https://orcid.org/0000-0002-4381-3250 S. Parameswari Sri Sairam Institute of Technology, India Lakshmi Sridevi Chennai Institute of Technology, India N. Janaki Vels Institute of Science Technology and Advanced Studies, India S. Gopinath Karpagam Institute of Technology, India Harishchander Anandaram Amrita Vishwa Vidyapeetam, India https://orcid.org/0000-0003-2993-5304 K. S. Shreenidhi Rajalakshmi Engineering College, India https://orcid.org/0000-0002-6844-0454 Samaya Pillai Iyengar Symbiosis Institute of Digital and Telecom Management, India https://orcid.org/0000-0002-8451-8936 Artificial Intelligence Augmented Integrated Development Environments for Boosting Programmer Productivity

AI is transforming software development with technologies that improve speed, quality, and productivity. AI-powered technologies and their use in software development are covered in this abstract. NLP algorithms help extract and categorize requirements from unstructured documents during requirements collecting and analysis. Machine learning algorithms forecast hazards and resource needs using past project data, improving planning and estimating. In addition, machine learning models trained on massive code repositories may produce code snippets and functions from natural language descriptions. AI algorithms produce test cases, prioritize test scenarios, and anticipate defect-prone code for testing and quality assurance. Automatic bug detection technologies use deep learning to spot bugs before they hit production. This research article brings in more insights about the various tools and softwares that are utilized in various stages of software development life cycle for efficient product development.
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Item Type: Book Section
Subjects: Computer Science Engineering > Programming Language
Domains: Computer Science Engineering
Depositing User: Mr IR Admin
Date Deposited: 20 Aug 2025 06:43
Last Modified: 20 Aug 2025 06:43
URI: https://ir.vistas.ac.in/id/eprint/10056

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