In this ever-changing software development scenario, one needs to make sure that code quality is paramount. Indeed, research demonstrates that poor code quality makes the code 15 times more susceptible to being defective and requires 124% longer to correct. The Oversights in Code are being caught up with most of the time by Automated Code Review Tools running on Large Language Models, but that generally accumulates to the pull request cycle in the form of redundant reviews and comments. CoTest is introduced as an end-to-end prototype for improving code quality in continuous development. It integrates with hosting platforms like GitHub to provide automated static code analysis, social code review features, and native support for multiple programming languages. Thus, the aim of CoTest is to reduce development time even further by monitoring code complexity and technical debt. The technology stack employed in this architecture includes Next.js, Node.js, Redis, and Tailwind CSS combined with linters like ESLint and Pylint. Two major techniques used for this process are the generation of AST and tree traversal for code analysis. It followed a more refined rating system for quality, depending on the severity of issues. Preliminary results show that CoTest can parse JavaScript and Python files in an effective way, in that it is able to locate the errors and provide complete insights on the quality of code, in the hope of shaping newly established software practices for start-up businesses and small tech companies.

Software companies and startups, in a world where software development evolves fast, end up with several predicaments that attack them in a triple onslaught to maintain the quality of code in payback technical debts and have security assured. Technical debts may lower the pace of development and, hence, not meet deadlines or condi- tions in the market. Most probably, Developers may spend more time fixing a backlog of issues caused by technical debt, leaving less time for new feature development and innovation. Code Review Automation is a way to relieve reviewers of routine eval- uations and give automatic feedback at review time. Such problems mostly contribute to a high cost of development, long project realization time, and that software being insecure and reduced in performance. In this regard, smaller teams face more errors and breaches in the code, since it is tough for them to properly caters to in-depth code reviews and vulnerability assessments at the stage of developing a product—especially for the startups in the question. Hence, for start-ups and companies with a small team, this might result in shored loss of time and capital. Therefore, one needs to put into these companies advanced code analysis tools, which will provide actionable insights and automate quality assurance procedures. Failing to lead on this results in creativity and development stifling.