Kyla H. Levin

Kyla H. Levin, Nicolas van Kempen, Emery D. Berger et al.

Debugging is a critical but challenging task for programmers. This paper proposes ChatDBG, an AI-powered debugging assistant. ChatDBG integrates large language models (LLMs) to significantly enhance the capabilities and user-friendliness of conventional debuggers. ChatDBG lets programmers engage in a collaborative dialogue with the debugger, allowing them to pose complex questions about program state, perform root cause analysis for crashes or assertion failures, and explore open-ended queries like "why is x null?". To handle these queries, ChatDBG grants the LLM autonomy to "take the wheel": it can act as an independent agent capable of querying and controlling the debugger to navigate through stacks and inspect program state. It then reports its findings and yields back control to the programmer. By leveraging the real-world knowledge embedded in LLMs, ChatDBG can diagnose issues identifiable only through the use of domain-specific reasoning. Our ChatDBG prototype integrates with standard debuggers including LLDB and GDB for native code and Pdb for Python. Our evaluation across a diverse set of code, including C/C++ code with known bugs and a suite of Python code including standalone scripts and Jupyter notebooks, demonstrates that ChatDBG can successfully analyze root causes, explain bugs, and generate accurate fixes for a wide range of real-world errors. For the Python programs, a single query led to an actionable bug fix 67% of the time; one additional follow-up query increased the success rate to 85%. ChatDBG has seen rapid uptake; it has already been downloaded more than 75,000 times.

Kyla H. Levin, Kyle Gwilt, Emery D. Berger et al.

The advent of large language models (LLMs) has paved the way for a new era of programming tools with both significant capabilities and risks, as the generated code lacks guarantees of correctness and reliability. Developers using LLMs currently face the difficult task of optimizing, integrating, and maintaining code generated by AI. We propose an embedded domain-specific language (DSL), Pythoness, to address those challenges. In Pythoness, developers program with LLMs at a higher level of abstraction. Rather than interacting directly with generated code, developers using Pythoness operate at the level of behavioral specifications when writing functions, classes, or an entire program. These specifications can take the form of unit tests and property-based tests, which may be expressed formally or in natural language. Guided by these specifications, Pythoness generates code that both passes the tests and can be continuously checked during execution. We posit that the Pythoness approach lets developers harness the full potential of LLMs for code generation while substantially mitigating their inherent risks. We describe our current prototype implementation of Pythoness and demonstrate that it can successfully leverage a combination of tests and code generation to yield higher quality code than specifications alone.