A Systematic Literature Review of Code Smell Detection Tools for JavaScript Systems

Abstract

JavaScript is one of the most popular programming languages. As projects grow, their code can become complex which leads to code smells, signs that can indicate problems. Many tools are available to detect and fix these issues, but we need a comprehensive summary of their most important features. This paper provides a systematic literature review of JavaScript code smell detection tools. We searched four databases (Scopus, ACM Digital Library, IEEE Xplore, and Springer) using a specific search string to find relevant studies. To refine the results, we applied a four-step selection process, reducing 1002 initial studies to 18 by removing duplicates, filtering metadata, and reviewing their full texts. We then used backward and forward snowballing to find more relevant studies, increasing the total number to 27 primary studies. Finally, we examined these studies to analyze the code smell detection tools they described. We identified 22 tools, many published in top software engineering venues, such as ICSE, MSR and TSE. We found that most tools use rule-based linting (55%), which is efficient but struggles with complex architectural smells. Dynamic analysis (23%) is underused and AI-driven detection is completely missing, despite its relevance in modern software engineering research. Researchers are also developing framework-specific tools for modern JavaScript practices and focusing on the detection of test smells (22%). Most tools available to practitioners detect only basic smells and ignore deeper design issues. Tool builders can address these gaps by combining static and dynamic analysis and creating more adaptable tools. For researchers, the lack of AI-driven detection and modern benchmark datasets presents an opportunity for progress.