MUMBAI, India, Aug. 10 -- Intellectual Property India has published a patent application (202641090677 A) filed by Aditya Institute Of Technology And Management; B. Ramesh; G. Vijay Kumar; and V. Kiran Kumar on July 25, 2026, for Artificial Intelligence-Based System And Method For Software Dependency Risk Prediction Using Execution Graph-Based Causal Learning.
Inventors include B. Ramesh; G. Vijay Kumar; and V. Kiran Kumar.
The application for the patent was published on July 31, 2026, under issue no. 31/2026.
Abstract: An artificial intelligence-based system (100) for software dependency risk prediction using execution graph-based causal learning is provided. A runtime execution collector (102) collects runtime execution data from a software system during execution, wherein the runtime execution data comprises interactions among a plurality of software components. An execution graph generator (104) generates a dynamic execution graph (200) based on the runtime execution data, wherein the dynamic execution graph (200) comprises a plurality of nodes representing software components and edges representing relationships among the software components. A feature engineering engine (106) extracts a plurality of predictive features (402) from the dynamic execution graph (200). A causal learning engine (108) applies a causal learning model (404) to identify one or more causal relationships between dependency modifications and software failures. A risk prediction engine (110) predicts a deployment risk score for a software deployment based on the one or more causal relationships.
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