MUMBAI, India, April 17 -- Intellectual Property India has published a patent application (202611022963 A) filed by Panipat Institute Of Engineering And Technology, Samalkha, Haryana, on Feb. 26, for 'a system and method for predictive software reliability assessment and risk classification using statistical feature distillation and machine learning models.'
Inventor(s) include Dr. Gurmeet Kaur; Prof. Poonam Panwar; Prof. Rupinder Singh; and Dr. Noopur Tyagi.
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 a computer-implemented system and method for predictive assessment and classification of software reliability using statistically validated feature distillation and machine learning-based models. The system acquires a plurality of software metrics associated with code quality, testing effectiveness, and operational performance, normalizes the collected metrics, and applies statistical analysis to identify a reduced subset of significant reliability indicators. The selected features are processed by a machine learning classification engine to generate a multi-level software reliability risk classification representing different degrees of operational stability. The invention further incorporates a robustness evaluation mechanism to ensure consistent and environment-agnostic prediction performance. By providing an automated, objective, and predictive reliability assessment framework, the invention enables informed software deployment, quality assurance, and operational decision-making across diverse software environments."
Disclaimer: Curated by HT Syndication.
