Users demand fast, seamless webpage experiences, yet developers often struggle to meet these expectations within tight constraints. Performance optimization, while critical, is a time-consuming and often manual process. One of the most complex tasks in this domain is modifying the Document Object Model (DOM), which is why this study focuses on it. Recent advances in Large Language Models (LLMs) offer a promising avenue to automate this complex task, potentially transforming how developers address web performance issues. This study evaluates the effectiveness of nine state-of-the-art LLMs for automated web performance issue resolution. For this purpose, we first extracted the DOM trees of 15 popular webpages (e.g., Facebook), and then we used Lighthouse to retrieve their performance audit reports. Subsequently, we passed the extracted DOM trees and corresponding audits to each model for resolution. Our study considers 7 unique audit categories, revealing that LLMs universally excel at SEO & Accessibility issues. However, their efficacy in performance-critical DOM manipulations is mixed. While high-performing models like GPT-4.1 delivered significant reductions in areas like Initial Load, Interactivity, and Network Optimization (e.g., 46.52% to 48.68% audit incidence reductions), others, such as GPT-4o-mini, notably underperformed, consistently. A further analysis of these modifications showed a predominant additive strategy and frequent positional changes, alongside regressions particularly impacting Visual Stability. Our findings define safe areas for automation (e.g., SEO and accessibility) and reveal the limits of DOM-level resolution, underscoring the need for hybrid, validated workflows. However, it critically underscores the need for careful model selection, understanding their specific modification patterns, and robust human oversight to ensure reliable web performance improvements.

Automating front-end development directly from design artifacts and textual requirements could accelerate iteration cycles and reduce implementation errors, yet most prior work addresses only a single modality (either design-to-code or text-to-code generation) without integrating complementary specifications. We propose a multi-agent framework that jointly reasons over user stories and Figma designs to synthesize complete React applications. The framework coordinates generation, validation, and repair through three architectural strategies: Supervisor (tool-calling) for centralized routing, Hierarchical for decomposed supervision, and Custom for deterministic workflow execution. Evaluated on four real-world projects (75 user stories) using six generator–judge model pairs (Claude, Gemini, GPT), the system achieves 54% full functional coverage and 58% full visual fidelity; including partial matches raises success rates to 77% and 85%, respectively. Architectural choice modestly affects quality (3–5 percentage-point variation) but substantially impacts cost: the Custom architecture reduces generator token usage by 21–65% compared to Hierarchical and Supervisor (tool-calling) configurations, while judge models consistently dominate overall cost (5.9× × more tokens on average than generators). To further enhance pipeline stability and reduce manual intervention, we introduce a lightweight repair toolkit comprising automated refusal retries, JSX sanitization, and template scaffolding resolves the majority of generation-stage failures without regeneration. Overall, these results demonstrate that multimodal, agentic frameworks can reliably automate front-end synthesis, though achieving full production-grade quality still requires human refinement and improved handling of complex interaction behaviors.

Software repositories contain a wealth of data about the software development process, such as source code, documentation, issue tracking, and commit histories. However, accessing and extracting meaningful insights from these data is timeconsuming and requires technical expertise, posing challenges for software practitioners, especially non-technical stakeholders like project managers. Existing solutions, such as software engineering chatbots leveraging LLMs, have demonstrated significant limitations in retrieving relevant data to answer user questions. In this paper, we introduce RepoChat, a web-based tool designed to answer repository-related questions by synergizing LLMs with knowledge graphs. RepoChat operates in two steps: (1) the Data Ingestion step, where it collects and constructs a knowledge graph from repository metadata, such as commits, issues, files and users; and (2) the Interaction step, where it takes the users natural language question, translates it into graph queries using an LLM, executes these queries against the knowledge graph, and generates a user-friendly response to the question using the query results as context. We evaluate RepoChat by conducting a user study in which participants asked a series of repository-related questions representing common developer intents. RepoChat achieved an accuracy of $90 \%$, correctly answering 36 out of 40 questions, demonstrating its effectiveness in accurately retrieving relevant information to answer user’s questions. RepoChat is available at https://repochattool.streamlit.app/, and its source code is accessible on Zenodo [1].