Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
@article{196194,
author = {Vijay V and Mrs. K. Krishna Veni and Ms. Divya. S},
title = {RAPID CODE QUALITY ANALYZER (RCQA) A Lightweight Multi-Language Static Analysis Platform for Academic and Small-Scale Development Environments},
journal = {International Journal of Innovative Research in Technology},
year = {2026},
volume = {12},
number = {11},
pages = {2642-2645},
issn = {2349-6002},
url = {https://ijirt.org/article?manuscript=196194},
abstract = {Code quality assurance is a fundamental requirement in modern software engineering to ensure maintainability, security, and reliability. Traditional manual review methods are time-consuming and inconsistent, while enterprise-grade static analysis tools such as SonarQube impose infrastructure complexity and licensing costs. This paper presents the design and implementation of the Rapid Code Quality Analyzer (RCQA), a lightweight, web-based static analysis system designed for academic institutions and small development teams.
RCQA supports six programming languages—Python, Java, JavaScript, TypeScript, C, and C++—using Abstract Syntax Tree (AST) analysis for Python and regex-based heuristic analysis for other languages. The system evaluates source code using metrics including Cyclomatic Complexity, Cognitive Complexity, Halstead Effort, Maintainability Index, Lines of Code, and security vulnerability detection. A weighted scoring model produces a unified quality score (0–100), classifying code from Enterprise Ready to High Risk.
The platform is implemented using FastAPI, SQLite, SQLAlchemy, JWT authentication, bcrypt hashing, and a responsive HTML/CSS/JavaScript frontend with Chart.js visualization. Experimental evaluation demonstrates accurate detection of complexity and security issues with efficient performance for typical academic-scale codebases. The study concludes that effective multi-language static analysis can be delivered using open-source technologies without enterprise overhead.},
keywords = {Static Code Analysis, Software Quality Metrics, Cyclomatic Complexity, Maintainability Index, FastAPI, Secure Coding, Multi-language Analysis.},
month = {April},
}
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