# Comparative Analysis of Chatbots Using Large Language Models for Web Development Tasks
## 6 Conclusions  
This study set out to determine the capabilities of LLM-based chatbots in generating code for web development tasks. The results demonstrate that these chatbots are quite proficient in handling complex, multi-step tasks such as creating HTML structures, designing CSS layouts, implementing PHP forms, and integrating with MySQL databases. While there is still room for improvement, the overall performance of the evaluated chatbots suggests that they can be valuable assistive tools for web developers, potentially boosting productivity and streamlining certain coding processes. A key objective was to explore the differences between conversational chatbots and programming assistants in this context. The findings reveal distinct strengths and weaknesses. Conversational models like ChatGPT and Copilot excelled in understanding prompts, generating diverse and user-friendly content, and delivering visually appealing designs. However, programming assistants like TabNine AI and BlackBox demonstrated stronger adherence to syntax rules and structural requirements. Interestingly, the study suggests that the ideal approach may be to integrate the output from conversational chatbots into integrated development environments (IDEs) using plugins, combining the strengths of both types of models for a seamless web development experience. One of the challenges addressed in this research was the lack of a standardized method for evaluating and selecting the most appropriate LLM chatbot for web development tasks. While no easy solution currently exists, this study provides a useful guideline for testing and comparing different chatbots across various criteria, such as accuracy, usability, creativity, and database integration. By following a structured evaluation process similar to the one outlined in this study, web developers can make informed decisions when choosing the most suitable chatbot assistant for their specific needs and project requirements. Beyond the academic scope, the implications of this research extend to applied web development practices. The findings suggest that integrating conversational chatbots within existing IDEs can significantly enhance productivity, allowing developers to focus on refining complex backend logic while benefiting from the chatbots' proficiency in generating clean, well-structured HTML and CSS. Furthermore, the study highlights that conversational chatbots can reduce development time in rapid prototyping phases and improve code creativity, while code-specific tools like BlackBox offer valuable support in repetitive coding tasks with an emphasis on correctness and security. This comparative analysis provides a roadmap for improving LLM-based coding tools, both in academic research and practical software development, and positions these tools as key enablers of the future of AI-assisted development. Despite the valuable insights gained from evaluating the chatbots on specific web development tasks, we acknowledge that the scope of tasks tested in this study was relatively small. Our focus was on seven common challenges in web development, which, while representative of certain aspects of the field, do not encompass the full spectrum of complexities and scenarios that developers encounter in real-world projects. This limitation may affect the generalizability of our findings to other programming tasks or domains. Future research should consider expanding the range of tasks to include more diverse and complex challenges, such as advanced backend development, security implementations, and performance optimization. The authors confirm contributions to the paper as follows: Study conception and design: P.S. and M.B. ; Data collection, analysis and interpretation of results, P.S. and M.B. ; Original draft manuscript preparation, P.S. ; Writing, review, and editing, P.S. and M.B. All authors have read and agreed to the published version of the manuscript.