MUMBAI, India, April 17 -- Intellectual Property India has published a patent application (202611022891 A) filed by Panipat Institute Of Engineering And Technology, Samalkha, Haryana, on Feb. 26, for 'an explainable machine-learning-based system and normalized composite metric for early-stage software fault prediction.'
Inventor(s) include Dr. Gurmeet Kaur; Prof. Dinesh C. Verma; Dr. Manjot Kaur Sidhu; and Dr. Satyajee Srivastava.
The application for the patent was published on April 17, under issue no. 16/2026.
According to the abstract released by the Intellectual Property India: "The present invention relates to an explainable machine-learning-based system for early-stage software fault prediction that enables proactive assessment of defect risk during the requirements and design phases of the software development lifecycle. The system integrates technical, human-centric, and process-related parameters into a normalized composite risk metric that mathematically balances requirements complexity against multiple mitigation capacity factors. The normalized metric is processed using an ensemble of machine-learning regression models to estimate software fault occurrence with high predictive accuracy and numerical stability. To enhance transparency and practical usability, the system incorporates explainability mechanisms that generate feature-importance and instance-level diagnostic insights identifying key drivers of software fault risk. The invention supports shift-left quality management, optimized resource allocation, and improved software reliability across diverse software development environments."
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