CodeScope: An Execution-based Multilingual Multitask Multidimensional Benchmark for Evaluating LLMs on Code Understanding and Generation (2311.08588v3)

Abstract: LLMs have demonstrated remarkable performance on assisting humans in programming and facilitating programming automation. However, existing benchmarks for evaluating the code understanding and generation capacities of LLMs suffer from severe limitations. First, most benchmarks are insufficient as they focus on a narrow range of popular programming languages and specific tasks, whereas real-world software development scenarios show a critical need to implement systems with multilingual and multitask programming environments to satisfy diverse requirements. Second, most benchmarks fail to consider the actual executability and the consistency of execution results of the generated code. To bridge these gaps between existing benchmarks and expectations from practical applications, we introduce CodeScope, an execution-based, multilingual, multitask, multidimensional evaluation benchmark for comprehensively measuring LLM capabilities on coding tasks. CodeScope covers 43 programming languages and eight coding tasks. It evaluates the coding performance of LLMs from three dimensions (perspectives): length, difficulty, and efficiency. To facilitate execution-based evaluations of code generation, we develop MultiCodeEngine, an automated code execution engine that supports 14 programming languages. Finally, we systematically evaluate and analyze eight mainstream LLMs and demonstrate the superior breadth and challenges of CodeScope for evaluating LLMs on code understanding and generation tasks compared to other benchmarks. The CodeScope benchmark and code are publicly available at https://github.com/WeixiangYAN/CodeScope.

Evaluation of Code Understanding and Generation by LLMs: Insights from the CodeScope Benchmark

LLMs have increasingly demonstrated their utility in automating aspects of software development, such as code generation and understanding. However, existing benchmarks are limited in scope concerning programming languages, tasks, and practical evaluation based on executable code. The paper "CodeScope: An Execution-based Multilingual Multitask Multidimensional Benchmark for Evaluating LLMs on Code Understanding and Generation" addresses these limitations by introducing a comprehensive benchmarking suite designed to rigorously evaluate LLMs on syntactic and semantic code challenges.

Key Features of CodeScope

CodeScope consists of a prolific span of 43 programming languages and encompasses eight tasks, classified under code understanding and generation. The benchmark fundamentally evaluates code proficiency of LLMs through actual execution dimensions, ensuring that generated code not only adheres to syntactic similarity but also executes correctly in practical scenarios. LLMs are evaluated multidimensionally across Length, Difficulty, and Efficiency dimensions, presenting a diverse evaluation landscape.

1. Multilingual and Multitask Suite: CodeScope includes languages ranging from Python to Delphi, encapsulating a variety of programming paradigms. This multilingual facet challenges the LLMs to generalize across structural and syntactic variants. Tasks like summarization, code translation, and repair enforce LLMs to exhibit understanding beyond simplistic syntactic translation.
2. Execution-based Evaluation: Previous reliance on n-gram metrics like BLEU for evaluation was limiting, only assessing surface-level similarity. CodeScope innovatively applies execution-based metrics supported by MultiCodeEngine, evaluating code on actual functional grounds with repercussions for correctness and efficiency.
3. Baseline and Dimension Analysis: The paper benchmarks mainstream LLMs such as GPT-4, LLaMA, and StarCoder on the set tasks under different contextual and task boundaries. We observe that models like WizardCoder excel in complex code structures, highlighting their capability in analyzing intricate logical constructs. However, models such as GPT-3.5, while proficient at easier problems, demonstrate limitations when tackling harder, real-world-inspired scenarios.

Implications and Future Directions

The implications of CodeScope span both practical and theoretical realms. Practically, the benchmark results facilitate an understanding of the current competencies and blind spots in how LLMs process code across languages and paradigms, informing future LLM architectural improvements and training methodologies. Theoretically, CodeScope sets a foundation for emerging evaluation strategies focusing on execution-centric metrics, underscoring the multidimensional competency assessments that simulate real-world software engineering challenges.

The paper concludes by suggesting potential future developments in AI and LLMs concerning code understanding and generation. These include augmenting datasets to cover more programming languages and paradigms further, enhancing execution environments to simulate even more complex application scenarios and refining evaluation metrics to account for nuanced programming attributes like optimization and maintainability.

Conclusion

Incorporating comprehensive benchmarks like CodeScope enriches the evaluation landscape for LLMs, driving advancements in their application to coding tasks. This benchmark is poised to inspire continued enhancements in the design and training of LLMs, steering them closer to fulfilling the multifaceted demands of real-world software development domains.