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CodeMind: A Framework to Challenge Large Language Models for Code Reasoning (2402.09664v4)

Published 15 Feb 2024 in cs.SE, cs.AI, cs.CL, and cs.PL

Abstract: Solely relying on test passing to evaluate LLMs for code synthesis may result in unfair assessment or promoting models with data leakage. As an alternative, we introduce CodeMind, a framework designed to gauge the code reasoning abilities of LLMs. CodeMind currently supports three code reasoning tasks: Independent Execution Reasoning (IER), Dependent Execution Reasoning (DER), and Specification Reasoning (SR). The first two evaluate models to predict the execution output of an arbitrary code or code the model could correctly synthesize. The third one evaluates the extent to which LLMs implement the specified expected behavior. Our extensive evaluation of nine LLMs across five benchmarks in two different programming languages using CodeMind shows that LLMs fairly follow control flow constructs and, in general, explain how inputs evolve to output, specifically for simple programs and the ones they can correctly synthesize. However, their performance drops for code with higher complexity, non-trivial logical and arithmetic operators, non-primitive types, and API calls. Furthermore, we observe that, while correlated, specification reasoning (essential for code synthesis) does not imply execution reasoning (essential for broader programming tasks such as testing and debugging): ranking LLMs based on test passing can be different compared to code reasoning.

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Evaluating LLMs' Code Reasoning Abilities with CodeMind

Introduction to CodeMind

CodeMind presents a novel framework designed specifically for evaluating LLMs' (LLMs) abilities in code reasoning, a critical aspect in assessing their programming capabilities. Unlike approaches that rely solely on test-case passing, CodeMind introduces a structured method to dissect and understand the intricate process of code synthesis and execution reasoning among LLMs. This framework incorporates three distinct tasks: Independent Execution Reasoning (IER), Dependent Execution Reasoning (DER), and Specification Reasoning (SR), each engineered to test various facets of LLMs' code understanding and predictive accuracy.

Key Findings from the CodeMind Evaluation

The comprehensive assessment of nine leading LLMs, spanning both general and programming-specific models, yields informative insights into the state of AI-driven coding assistance. Among the key observations:

  • Understanding of Code Constructs: LLMs demonstrated a commendable grasp over basic code constructs and the ability to track input transformations to outputs, markedly for simpler code specimens and those they could accurately synthesize.
  • Limitations in Complex Code Reasoning: The LLMs' performance significantly diminished when confronted with complex code structures, intricate logical/arithmetic operations, and the utilization of API calls, revealing a noticeable gap in handling advanced programming concepts.
  • Discrepancy Between Specification and Execution Reasoning: An intriguing revelation from the paper is the disparity between LLMs' abilities to reason based on specifications versus their capacity to predict execution outcomes. This highlights a potential misalignment in the evaluation metrics when ranking LLMs purely on their code generation capabilities.

Technical Contributions and Framework Utility

The introduction of CodeMind is a substantial contribution to the field, offering an open-source platform for the collaborative enhancement of code reasoning benchmarks. The framework's design encompasses:

  • A Trio of Inductive Code Reasoning Tasks: Each task within CodeMind targets a specialized aspect of code reasoning, from predicting execution outcomes independently or relative to synthesized code, to adhering to given specifications.
  • Extensive Ground-Theory Evaluation: The deployment of CodeMind in evaluating a broad array of LLMs across diverse programming benchmarks transitions the theoretical model into a practical tool for deeper insights into LLM capabilities.
  • Insightful Analyses for Future Development: The paper meticulously catalogs the challenges LLMs face in code reasoning, laying down a roadmap for future enhancements in both LLM training and benchmark development.

Implications and Future Directions

The findings from the CodeMind evaluations serve both theoretical and practical advancements in the deployment of LLMs for coding tasks. The observed limitations underscore the necessity for targeted improvements in LLM training methodologies, especially for handling complex code structures and logical constructs. Moreover, the disparity between specification reasoning and execution reasoning suggests the need for a more holistic approach in LLM evaluation, considering both code generation and reasoning capabilities.

Looking ahead, expanding CodeMind to encompass additional code reasoning tasks appears to be a promising direction. These could potentially include challenges that test LLMs' understanding of variable scope, data flow across code segments, and optimization reasoning. Such extensions would not only refine the evaluation of LLMs but also pave the way for developing more sophisticated models capable of true programming mastery.

In essence, CodeMind stands as a pivotal step toward achieving a more nuanced and thorough understanding of LLMs' programming prowess, signaling a move towards more sophisticated and capable AI-driven coding assistants in the future.

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Authors (4)
  1. Changshu Liu (4 papers)
  2. Shizhuo Dylan Zhang (5 papers)
  3. Reyhaneh Jabbarvand (10 papers)
  4. Ali Reza Ibrahimzada (6 papers)