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Large Language Models for Code (or code LLMs) are increasingly gaining popularity and capabilities, offering a wide array of functionalities such as code completion, code generation, code summarization, test generation, code repair, refactoring, translation, and more. To leverage code LLMs to their full potential, developers must provide code-specific contextual information to the models. These are typically derived and distilled using program analysis tools. However, there exists a significant gap—these static analysis tools are often language-specific and come with a steep learning curve, making their effective use challenging. These tools are tailored to specific program languages, requiring developers to learn and manage multiple tools to cover various aspects of their code base. Moreover, the complexity of configuring and integrating these tools into the existing development environments adds an additional layer of difficulty. This challenge limits the potential benefits that could be gained from the more widespread and effective use of static analysis in conjunction with code LLMs.

In this technical briefing, we present Codellm-Devkit (CLDK)—an open-source library that significantly simplifies the process of performing program analysis at various levels of granularity. As a Python-based library, CLDK offers developers an intuitive and user-friendly interface, making it incredibly easy to provide rich program analysis context to code LLMs. With this library, developers can effortlessly integrate detailed, code-specific insights that enhance the operational efficiency and effectiveness of LLMs in coding tasks. This hands-on session will enable participants to perform static analysis to build LLM-based solutions for coding tasks such as: (1) code generation, (2) code summarization, and (3) test generation across different programming languages. Through practical exercises, developers will gain hands-on experience in enhancing the functionality and applicability of code LLMs using CLDK’s APIs.