REPOEXEC: Evaluate Code Generation with a Repository-Level Executable Benchmark
Summary
Paper digest
What problem does the paper attempt to solve? Is this a new problem?
The paper aims to introduce REPOEXEC, a novel Python code generation benchmark at the repository level with executable capabilities, designed to evaluate the alignment of generated code with developer intent and its correctness . This benchmark evaluates how different language models leverage code dependencies when generating code and provides insights into the models' ability to utilize provided dependencies effectively . The paper addresses the challenge of evaluating code generation models based on their ability to reuse dependencies, ensure functional correctness, and align with developer intent, highlighting the importance of context size in impacting the final results . While the specific focus of the paper is on code generation evaluation techniques, the problem of assessing code generation models' performance concerning dependency usage and correctness is not entirely new but is addressed in a novel way through the introduction of the REPOEXEC benchmark .
What scientific hypothesis does this paper seek to validate?
This paper aims to validate the scientific hypothesis that instruction-tuned models in code generation demonstrate proficiency in utilizing dependencies and debugging, while pretrained language models (LLMs) excel in functional correctness . The study evaluates how different LLMs leverage code dependencies when generating code and highlights the impact of context size on the final results . Additionally, the research introduces an instruction-tuning dataset that enhances dependency invocation accuracy and output correctness, even with limited context, providing valuable insights for future advancements in code generation models .
What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?
The paper "REPOEXEC: Evaluate Code Generation with a Repository-Level Executable Benchmark" introduces several novel ideas, methods, and models in the field of code generation evaluation:
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Instruction-Tuning Dataset: The paper introduces an instruction-tuning dataset that enhances dependency invocation accuracy and output correctness, even with limited context. This dataset allows for the integration of additional context types and mitigates large token length constraints, providing valuable insights for future research in code generation .
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REPOEXEC Benchmark: The paper introduces the REPOEXEC benchmark, a Python code generation benchmark at the repository level with executable capabilities. This benchmark is designed to evaluate the alignment of generated code with developer intent and its correctness. It compares the performance of pretrained language models (LLMs) with instruction-tuned models, highlighting the proficiency of instruction-tuned models in utilizing dependencies and debugging .
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Dependency Invocation Rate (DIR): The paper introduces the Dependency Invocation Rate (DIR) as a metric to assess the models' ability to utilize provided dependencies in accordance with human intent. A higher DIR indicates that the model successfully incorporates a larger proportion of the provided dependencies into the generated code, demonstrating a better understanding of the dependencies' relevance and intended usage .
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Model Comparison: The paper evaluates various models such as StarCoder, CodeLlama, WizardCoder, and Mixtral on the REPOEXEC benchmark before and after coverage enhancement. It compares the performance of these models in terms of Pass@k and DIR results, showcasing the effectiveness of different models in generating code aligned with developer intent and utilizing dependencies .
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Efficiency and Effectiveness: The paper highlights the efficiency of instruction-tuned models in practice, as they utilize single-turn generation compared to self-refinement through multi-round debugging. This makes instruction-tuned models more efficient while still ensuring functional correctness and dependency utilization .
Overall, the paper's contributions provide valuable insights into improving code generation models, enhancing dependency invocation accuracy, and aligning generated code with developer intent, paving the way for more capable and reliable models in the field of code generation evaluation. The paper "REPOEXEC: Evaluate Code Generation with a Repository-Level Executable Benchmark" introduces several characteristics and advantages compared to previous methods in the field of code generation evaluation:
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Instruction-Tuning Dataset: The paper introduces an instruction-tuning dataset that enhances dependency invocation accuracy and output correctness, even with limited context. This dataset allows for the integration of additional context types and mitigates large token length constraints, providing valuable insights for future research in code generation .
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REPOEXEC Benchmark: The paper presents the REPOEXEC benchmark, a Python code generation benchmark at the repository level with executable capabilities. This benchmark evaluates the alignment of generated code with developer intent and its correctness. It highlights that pretrained language models (LLMs) excel in functional correctness, while instruction-tuned models demonstrate proficiency in utilizing dependencies and debugging, offering a more comprehensive evaluation approach .
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Dependency Invocation Rate (DIR): The paper introduces the Dependency Invocation Rate (DIR) as a metric to assess the models' ability to utilize provided dependencies in accordance with human intent. A higher DIR indicates successful incorporation of a larger proportion of dependencies into the generated code, showcasing a better understanding of dependency relevance and intended usage. This metric provides a more nuanced evaluation of code generation models compared to previous methods .
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Efficiency and Effectiveness: The paper highlights the efficiency of instruction-tuned models in practice, as they utilize single-turn generation compared to self-refinement through multi-round debugging. This approach makes instruction-tuned models more efficient while ensuring functional correctness and dependency utilization. Additionally, the paper emphasizes the importance of maintaining high-quality test cases for evaluation purposes, underscoring the effectiveness of instruction-tuning models in generating correct solutions aligned with developer intent .
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Model Comparison and Improvement: The paper evaluates various models such as StarCoder, CodeLlama, WizardCoder, and Mixtral on the REPOEXEC benchmark before and after instruction tuning. It demonstrates that instruction-tuned models show significant improvement in performance metrics like Pass@1 and DIR compared to pretrained versions, showcasing the effectiveness of instruction tuning in enhancing code generation models .
Overall, the characteristics and advantages of the proposed methods in the paper provide a more nuanced and effective approach to evaluating code generation models, emphasizing the importance of dependency utilization, alignment with developer intent, and efficiency in generating correct code solutions.
Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?
Several noteworthy researchers have contributed to related research on code generation evaluation and instruction tuning in the field. Some of the key researchers mentioned in the paper "REPOEXEC: Evaluate Code Generation with a Repository-Level Executable Benchmark" include:
- Roziere et al. [2023]
- Li et al. [2023]
- Lozhkov et al. [2024]
- Jiang et al. [2024]
- Luo et al. [2023]
- Javaheripi et al. [2023]
- Gunasekar et al. [2023]
- Guo et al. [2024]
- Chen et al. [2021]
- Hu et al. [2021]
These researchers have contributed to the development and evaluation of various models for code generation, instruction tuning, and debugging processes in the context of code dependencies and correctness .
The key to the solution mentioned in the paper involves utilizing instruction-tuning datasets to enhance dependency invocation accuracy and output correctness, even with limited context. By fine-tuning base Language Models (LLMs) with instruction prompts derived from a dataset of functions and dependencies, models like StarCoder, StarCoder2, and CodeLlama-13b-Python have shown improvements in both Pass@1 (functional correctness) and Dependency Invocation Rate (DIR) metrics. This approach aims to improve the model's ability to reuse dependencies effectively and ensure the correctness of the generated code, addressing issues related to technical debt and code smell .
How were the experiments in the paper designed?
The experiments in the paper were designed to evaluate code generation models using a repository-level executable benchmark called REPOEXEC. The experiments aimed to assess the alignment of generated code with developer intent and its correctness . The evaluation involved comparing the performance of different models before and after instruction tuning for dependency calls, focusing on factors like Pass@1, Pass@5, and DIR scores . The study also introduced an instruction-tuning dataset to enhance dependency invocation accuracy and output correctness, enabling the integration of additional context types and mitigating large token length constraints . Additionally, the experiments explored the impact of context size on the final results, highlighting that retaining the full context of dependencies yielded the best performance across all models .
What is the dataset used for quantitative evaluation? Is the code open source?
The dataset used for quantitative evaluation in the context is called REPOEXEC . This dataset is utilized for evaluating code generation at the repository-level scale, focusing on executability and correctness of the generated code. The REPOEXEC benchmark provides a system that automates verification of requirements and includes a mechanism for dynamically generating high-coverage test cases to assess the functionality of the generated code .
Regarding the openness of the code, the context does not explicitly mention whether the code used in the REPOEXEC dataset is open source or not. It primarily focuses on the evaluation methodology and the benchmark itself, emphasizing the assessment of code generation at the repository-level scale .
Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.
The experiments and results presented in the paper provide substantial support for the scientific hypotheses that need to be verified. The study introduces a novel Python code generation benchmark called REPOEXEC, designed to assess the alignment of generated code with developer intent and its correctness . The experiments conducted reveal that pretrained language models (LLMs) excel in functional correctness, while instruction-tuned models demonstrate proficiency in utilizing dependencies and debugging . This comparison between LLMs and instruction-tuned models helps evaluate the effectiveness of different approaches in code generation.
Moreover, the study highlights the limitations of existing models in effectively reusing provided dependencies, which can lead to technical debt and code smell issues . By introducing an instruction-tuning dataset that enhances dependency invocation accuracy and output correctness, the research provides valuable insights into improving model capabilities and reliability in code generation tasks . The findings emphasize the importance of maintaining high-quality test cases for evaluation purposes, underscoring the need for comprehensive evaluation metrics in this domain .
Furthermore, the paper discusses the impact of coverage enhancement on the effectiveness of test cases in identifying incorrect generated solutions . Weak unit tests, even when human-written, may inadvertently validate incorrect implementations, highlighting the importance of enhancing test coverage to ensure more accurate evaluations . This analysis contributes to the understanding of the challenges and considerations involved in evaluating code generation models within repository-level contexts.
Overall, the experiments and results presented in the paper offer a robust foundation for verifying scientific hypotheses related to code generation. The comparison between pretrained models and instruction-tuned models, along with insights on dependency utilization, debugging proficiency, and test case quality, collectively contribute to advancing research in code generation and model evaluation .
What are the contributions of this paper?
The paper makes several key contributions in the field of code generation:
- Introducing a repository-level executable benchmark called REPOEXEC to evaluate the alignment of generated code with developer intent and its correctness .
- Proposing an instruction-tuning dataset that enhances dependency invocation accuracy and output correctness, enabling the integration of additional context types and mitigating large token length constraints .
- Providing valuable evaluation techniques and insights to drive the development of more capable and reliable models in code generation research .
What work can be continued in depth?
Further research in the field of code generation can be expanded by delving deeper into the evaluation techniques and insights provided by the REPOEXEC benchmark. This benchmark introduces an instruction-tuning dataset that enhances dependency invocation accuracy and output correctness, even with limited context, paving the way for integrating additional context types and overcoming token length constraints . Additionally, exploring how different language models leverage code dependencies and the impact of context size on the final results can offer valuable insights for future research in code generation . Furthermore, investigating the effectiveness of instruction-tuning models in reusing dependencies, ensuring functional correctness, and addressing technical debt and code smell issues can be a promising avenue for further exploration .