Chart generation aims to generate code to produce charts satisfying the desired visual properties, e.g., texts, layout, color, and type. It has great potential to empower the automatic professional report generation in financial analysis, research presentation, education, and healthcare. In this work, we build a vision-language model (VLM) based multi-agent framework for effective automatic chart generation. Generating high-quality charts requires both strong visual design skills and precise coding capabilities that embed the desired visual properties into code. Such a complex multi-modal reasoning process is difficult for direct prompting of VLMs. To resolve these challenges, we propose METAL, a multi-agent framework that decomposes the task of chart generation into the iterative collaboration among specialized agents. METAL achieves 5.2% improvement over the current best result in the chart generation task. The METAL framework exhibits the phenomenon of test-time scaling: its performance increases monotonically as the logarithmic computational budget grows from 512 to 8192 tokens. In addition, we find that separating different modalities during the critique process of METAL boosts the self-correction capability of VLMs in the multimodal context.

While developers routinely use many natural language elements (e.g., todo comments) for communication, the semantic content of these elements is often neglected by software engineering techniques and tools. Additionally, as software evolves and development teams re-organize, these natural language elements are frequently forgotten, or just become outdated, imprecise and irrelevant. We envision several techniques, which combine natural language processing and program analysis, to help developers maintain their todo comments. Specifically, we propose techniques to synthesize code from comments, make comments executable, answer questions in comments, improve comment quality, and detect dangling comments.

Natural language elements (e.g., API comments, todo comments) form a substantial part of software repositories. While developers routinely use many natural language elements (e.g., todo comments) for communication, the semantic content of these elements is often neglected by software engineering techniques and tools. Additionally, as software evolves and development teams re-organize, these natural language elements are frequently forgotten, or just become outdated, imprecise and irrelevant. We envision several techniques, which combine natural language processing and program analysis, to help developers maintain their todo comments. Specifically, we propose techniques to synthesize code from comments, make comments executable, answer questions in comments, improve comment quality, and detect dangling comments.