MUMBAI, India, Jan. 2 -- Intellectual Property India has published a patent application (202541123137 A) filed by Malla Reddy (MR) Deemed to be University; Malla Reddy College Of Engineering And Technology; Malla Reddy Vishwavidyapeeth; Malla Reddy University; Malla Reddy Engineering College For Womens; and Dr. Deena Babu Mandru, Medchal-Malkajgiri, Telangana, on Dec. 6, 2025, for 'generative ai model for automatic code documentation and refactoring.'
Inventor(s) include Dr. Deena Babu Mandru; Dr A Nagaraju; Dr. Kunchala Little Flower; Dr. G. Mohan Ram; Mr. Votte Rajashekhar; and Ms. Mittapally Anusha.
The application for the patent was published on Jan. 2, under issue no. 01/2026.
According to the abstract released by the Intellectual Property India: "Software projects often tend to add on to themselves complex, poorly documented, or inconsistently structured code as they grow. This poses a big problem to developers who are bound to sustain, extend or debug systems without a clear understanding of how it works or its purpose. These problems are solved by a generative AI model that suggests automatic code documentation and refactoring with the purpose to analyse the source code, comprehend its logical structure, and generate human-readable explanations and optimized versions of the code. The syntax, control flow, data dependencies and design patterns are analysed in the model to create correct summaries about what the code performs, how functions interrelate, and why particular operations are performed. Such summaries allow developers to gain very fast insight into legacy codebases, or modules they are not used to. Along with documentation, the model does automated refactoring, locating redundant sections, enhancing naming, reorganizing the complex logic and proposing more efficient or maintainable constructs without changing the original functionality. Current machine learning algorithms allow the model to be trained on the large and heterogeneous repositories of code; as a result, it can be trained on various programming languages, frameworks, and architectural styles. Developer feedback is also used to enhance the performance of the model so that it can adjust to project-specific conventions and code standards. The system enables faster development processes and the reduction of technical debt through automation of documentation systems and optimization of structure and adaptation of learning, and ensures improved long-term maintainability as applied to software applications."
Disclaimer: Curated by HT Syndication.
