Abstract: This article describes about the project implementation of "Recommendation System for Validating code and Optimal Refactoring" and its outcomes for the problems mentioned. This project enhances coding practices by suggesting clean code snippets and with enhanced scoring mechanism. Initially, it identifies code issues using static code analysis APIs in languages like Java, Python, HTML, CSS and JavaScript. And then uses natural language processing techniques and libraries like NumPy and scikit-learn to recommend context-specific code solutions. This innovative system integrates with popular IDEs, supports multiple languages, can be customizable and developed on huge dataset training, significantly improving code validation and refactoring processes.
Keywords: Static code analysis APIs, Programming language linters, NumPy, Pandas, Scikit-learn, Collaborative Filtering Algorithm, NLP techniques (Stemming), Vector embeddings, Cosine similarity, TF-IDF algorithm.
Cite:
Koteswara Rao Velpula, Hema Pavuluri, Poojitha Neeluri, Anushka Pappala, Mounika Narra,"Recommendation System for Code Validation and Optimal Refactoring", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 3, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13313.
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DOI:
10.17148/IJARCCE.2024.13313
