REPOEXEC: Evaluate Code Generation with a Repository-Level Executable Benchmark

Nam Le Hai, Dung Manh Nguyen, Nghi D. Q. Bui·June 17, 2024

Summary

The paper presents REPOEXEC, a repository-level executable benchmark for evaluating code generation models, focusing on executability, functional correctness, and dependency management. It introduces a new dataset that enhances models' ability to handle dependencies and tests models' alignment with developer intent. Instruction-tuned models, like StarCoder and GPT-3.5, excel in dependency utilization, while pretrained models struggle. REPOEXEC assesses code functionality through metrics like Pass@k and Dependency Invocation Rate, and demonstrates the importance of comprehensive evaluation for real-world applications. The study also highlights the impact of context, fine-tuning, and multi-round debugging on model performance, with a focus on the CodeLLM community. The benchmark, dataset, and source code are publicly available for further research.

Key findings

12

Paper digest

Q1. What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of effectively utilizing dependencies in code generation by introducing an instruction-tuning dataset to fine-tune language models (LLMs) for better dependency invocation accuracy and output correctness . This problem is not entirely new, as existing models, particularly pretrained LLMs, have struggled to efficiently reuse provided dependencies, leading to issues like technical debt and code smell . The paper's focus on enhancing the model's ability to reuse dependencies and ensure functional correctness through instruction-tuning is a novel approach to improving code generation performance .


Q2. What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis that instruction-tuning models in code generation, which utilize single-turn generation with instruction prompts, can enhance the model's ability to reuse dependencies, ensure functional correctness of the output, and improve efficiency compared to self-refinement models that involve multi-round debugging . The study focuses on evaluating how different language models leverage code dependencies when generating code, highlighting the impact of context size on the final results . Additionally, the paper introduces an instruction-tuning dataset that enhances dependency invocation accuracy and output correctness, even with limited context, offering potential for more efficient processing and reduced computational costs .


Q3. 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 innovative ideas, methods, and models in the field of code generation evaluation and improvement:

  1. Instruction-Tuning Dataset: The paper proposes an instruction-tuning dataset that focuses on fine-tuning base Large Language Models (LLMs) with a specific emphasis on code dependencies. This dataset is designed to enhance the accuracy of dependency invocation and output correctness, even with limited context .

  2. Instruction-Tuned Models: The study demonstrates that instruction-tuned models excel in utilizing provided dependencies and demonstrating debugging capabilities. These models show proficiency in leveraging given dependencies to provide correct solutions, addressing edge or corner cases that pretrained models may overlook. Despite their high Dependency Invocation Rate (DIR), instruction-tuned models may sometimes produce overly complex code, leading to incorrect solutions .

  3. Multi-Round Debugging for Code Generation: The paper explores the concept of self-refinement through multi-round debugging to enhance code generation performance. Models like WizardCoder, GPT3.5, and CodeLlama-13b-Python are subjected to error output logs and asked to fix errors through multiple rounds of debugging. This process results in significant improvements in pass rates and DIR, particularly for GPT-3.5 and WizardCoder, showcasing their high capacity for debugging and improvement in correctness .

  4. Impact of Context Size: The research highlights that the size of the context significantly impacts the final results of code generation models. It is noted that models fine-tuned with a small context can achieve results comparable to those obtained with a full context, offering potential for more efficient processing and reduced computational costs. This allows for the integration of various other types of context, enhancing the model's ability to reuse dependencies and ensure functional correctness of the output .

  5. Challenges with Existing Models: The paper identifies limitations in existing models, particularly in effectively reusing provided dependencies, potentially leading to technical debt and code smell issues. Pretrained LLMs are noted to excel in functional correctness but may struggle to reuse dependencies efficiently, sometimes duplicating the implementation from the given context, resulting in redundancy and code quality issues .

Overall, the paper introduces a comprehensive evaluation framework, novel datasets, and methodologies to enhance code generation models' ability to align with developer intent, improve functional correctness, and effectively utilize code dependencies for more reliable and applicable CodeLLMs in real-world scenarios. The paper "REPOEXEC: Evaluate Code Generation with a Repository-Level Executable Benchmark" introduces novel characteristics and advantages compared to previous methods in the field of code generation evaluation and improvement:

  1. Instruction-Tuning Dataset: The paper proposes an instruction-tuning dataset that focuses on fine-tuning base Large Language Models (LLMs) with a specific emphasis on code dependencies. This dataset enhances the accuracy of dependency invocation and output correctness, even with limited context .

  2. Improved Dependency Utilization: Instruction-tuned models demonstrate a higher capacity for utilizing given dependencies compared to foundation models. They excel in addressing edge or corner cases that foundation models may overlook, showcasing proficiency in leveraging dependencies for more accurate solutions .

  3. Multi-Round Debugging for Code Generation: The study explores self-refinement through multi-round debugging to enhance code generation performance. Models like WizardCoder, GPT3.5, and CodeLlama-13b-Python undergo multiple rounds of debugging, resulting in significant improvements in pass rates and Dependency Invocation Rate (DIR), particularly for GPT-3.5 and WizardCoder .

  4. Impact of Context Size: The research highlights that the size of the context significantly influences the final results of code generation models. Models fine-tuned with a small context can achieve results comparable to those obtained with a full context, offering potential for more efficient processing and reduced computational costs .

  5. Instruction-Tuning with Code Dependencies: The paper introduces an instruction-tuning dataset for fine-tuning base LLMs, enhancing dependency invocation accuracy and output correctness. This approach improves the model's ability to reuse dependencies and ensure functional correctness of the output, with significant improvements in Pass@1 and DIR after instruction tuning .

In summary, the paper's innovative methods, such as instruction-tuning datasets, multi-round debugging, and context size impact analysis, offer significant advancements in code generation evaluation by enhancing dependency utilization, debugging capabilities, and overall model performance compared to previous methods.


Q4. 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 related research papers exist in the field of code generation and understanding. Noteworthy researchers in this field include Yue Wang, Hung Le, Akhilesh Deepak Gotmare, Nghi DQ Bui, Junnan Li, Steven CH Hoi, Frank F Xu, Uri Alon, Graham Neubig, Vincent Josua Hellendoorn, Pengcheng Yin, Bowen Deng, Edgar Chen, Bogdan Vasilescu, Fengji Zhang, Bei Chen, Jacky Keung, Jin Liu, Daoguang Zan, Yi Mao, Jian-Guang Lou, Weizhu Chen, Kechi Zhang, Jia Li, Ge Li, Xianjie Shi, Zhi Jin, Tianyu Zheng, Ge Zhang, Tianhao Shen, Xueling Liu, Bill Yuchen Lin, Jie Fu, and Xiang Yue .

The key to the solution mentioned in the paper "REPOEXEC: Evaluate Code Generation with a Repository-Level Executable Benchmark" is the introduction of a novel benchmark called REPOEXEC. This benchmark focuses on three main aspects: executability, functional correctness through automated test case generation with high coverage rate, and carefully crafted cross-file contexts to accurately generate code. The paper explores a controlled scenario where developers specify necessary code dependencies, challenging the model to integrate these accurately. The experiments conducted show that while pretrained LLMs outperform instruction-tuned models in correctness, the latter excel in utilizing provided dependencies and demonstrating debugging capabilities. Additionally, the paper introduces a new instruction-tuned dataset that focuses on code dependencies, enhancing CodeLLMs' ability to leverage these dependencies effectively .


Q5. 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 focused on assessing the alignment of generated code with developer intent and its correctness . The evaluation metrics used in the experiments included Pass@k to measure the functional correctness of generation outputs and Dependency Invocation Rate (DIR) to assess the models' ability to utilize provided dependencies in accordance with human intent . The experiments involved evaluating various language models (LLMs) on REPOEXEC to understand how different models leverage code dependencies when generating code . The experiments also included instruction-tuning models to enhance dependency invocation accuracy and output correctness, even with limited context . Additionally, the experiments explored the effectiveness of self-refinement capabilities in enhancing generation performance through multi-round debugging . The experiments aimed to highlight the importance of maintaining high-quality test cases for evaluation purposes and to address limitations in effectively reusing provided dependencies in code generation models .


Q6. What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is called REPOEXEC . The study mentions that the authors will publicly release this dataset . This indicates that the code used in the evaluation is likely to be open source.


Q7. 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 strong support for the scientific hypotheses that needed verification. The study introduces REPOEXEC, a Python code generation benchmark that evaluates 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 pretrained and instruction-tuned models helps validate the hypothesis regarding the effectiveness of instruction-tuning in enhancing dependency invocation accuracy and output correctness .

Furthermore, the evaluation metrics used in the study, such as Pass@k and Dependency Invocation Rate (DIR), provide quantitative measures to assess the models' performance in generating code aligned with human intent and utilizing provided dependencies effectively . The results show improvements in Pass@1 and DIR after instruction tuning, indicating that the models were able to better incorporate dependencies into the generated code, supporting the hypothesis that instruction-tuning can enhance the model's ability to reuse dependencies and ensure functional correctness .

Moreover, the analysis of different context sizes, such as Full context, Medium context, and Short context, demonstrates how the size of the context significantly impacts the final results in code generation tasks . The findings reveal that utilizing Short context reduces the occurrence of empty function generation by the model and improves the models' ability in dependency calls, supporting the hypothesis that context size plays a crucial role in the effectiveness of code generation models .

In conclusion, the experiments and results presented in the paper provide substantial evidence to support the scientific hypotheses related to code generation, model performance, dependency utilization, and context size impact on code generation tasks. The comprehensive evaluation and analysis conducted in the study contribute to advancing the understanding of how different models leverage code dependencies and generate code aligned with developer intent .


Q8. What are the contributions of this paper?

The paper "REPOEXEC: Evaluate Code Generation with a Repository-Level Executable Benchmark" makes several key contributions in the field of code generation evaluation:

  • Introduction of REPOEXEC: The paper introduces REPOEXEC, a novel benchmark designed to evaluate code generation at the repository-level scale. It focuses on aspects such as executability, functional correctness through automated test case generation, and accurately generating code based on carefully crafted cross-file contexts .
  • Comparison of Pretrained and Instruction-Tuned Models: The study compares pretrained language models (LLMs) with instruction-tuned models in terms of functionality and alignment with developer intent. While pretrained LLMs excel in functional correctness, instruction-tuned models demonstrate proficiency in utilizing dependencies and debugging effectively .
  • 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 improves the model's ability to leverage dependencies effectively .
  • Improvements in Model Performance: The research demonstrates that instruction-tuning models show improvements in both Pass@1 and DIR metrics after tuning with the dataset. There is a slight increase in Pass@k for all models, while DIR scores significantly improve, reaching the highest scores compared to other models after tuning .
  • Efficiency and Cost Reduction: The study highlights that the use of small context in instruction-tuning models can lead to more efficient processing, reduced computational costs, and comparable results to those obtained with full context. This efficiency makes instruction-tuning models more practical for real-world applications .

Q9. What work can be continued in depth?

To delve deeper into the field of code generation and evaluation, several avenues for further exploration can be pursued based on the existing research and benchmarks:

  1. Enhancing Code Generation Models: Further research can focus on enhancing Code Large Language Models (CodeLLMs) to improve their ability to generate executable and functionally correct code at a repository-level scale . This could involve refining the models to better understand and integrate code dependencies accurately, leading to more reliable and applicable CodeLLMs for real-world scenarios.

  2. Automated Test Case Generation: There is potential to explore automated test case generation with high coverage rates to ensure the functional correctness of generated code . By developing methodologies or frameworks that can automatically generate comprehensive test cases, the reliability and robustness of code generation systems can be further improved.

  3. Cross-File Contexts in Code Generation: Research can focus on crafting cross-file contexts to accurately generate code, especially in scenarios where code spans multiple files or modules . By understanding and leveraging the relationships between different parts of a codebase, models can be trained to generate code that aligns with the developer's intent across various files.

  4. Instruction-Tuned Models and Code Dependencies: Investigating the performance of instruction-tuned models in utilizing provided dependencies and demonstrating debugging capabilities can be a valuable area of study . Comparing the effectiveness of instruction-tuned models versus pretrained LLMs in leveraging code dependencies could provide insights into the strengths and weaknesses of different model architectures.

  5. Dataset Expansion and Fine-Tuning: Expanding datasets and fine-tuning CodeLLMs on instruction-tuned datasets that focus on code dependencies could lead to improved model capabilities in leveraging dependencies effectively . By curating datasets that emphasize code dependencies, researchers can train models that are better equipped to understand and utilize external code resources.

In summary, further research in code generation and evaluation can focus on enhancing model capabilities, improving test case generation, exploring cross-file contexts, comparing different model architectures, and expanding datasets for fine-tuning models to better leverage code dependencies. These avenues can contribute to the development of more advanced and reliable code generation systems.

Tables

5

Introduction
Background
Evolution of code generation models
Importance of executable benchmarks
Objective
To evaluate code generation models on executability, functional correctness, and dependency management
Addressing gaps in current evaluation methods
Method
Data Collection
New Dataset
Description and creation process
Focus on dependency management and developer intent
Code Generation Models
Selection of instruction-tuned (e.g., StarCoder, GPT-3.5) and pretrained models
Data Preprocessing
Cleaning and standardization of repository data
Handling of dependencies and context
Evaluation Metrics
Pass@k: Assessing functional correctness
Dependency Invocation Rate: Dependency management evaluation
Performance Analysis
Instruction-Tuned Models
Dependency utilization strengths
Pretrained Models
Challenges and limitations in dependency management
Context and Fine-Tuning Impact
Effect of context on model performance
Role of fine-tuning in improving executability
Multi-Round Debugging
Community-driven debugging process (CodeLLM)
Lessons learned and implications for model improvement
Results and Discussion
Comparative analysis of model performance
Importance of comprehensive evaluation for real-world applications
Conclusion
REPOEXEC benchmark's contribution to the field
Recommendations for future model development
Public availability of benchmark, dataset, and source code
Future Work
Directions for further research and improvements
Potential applications and implications for industry practices
Basic info
papers
software engineering
artificial intelligence
Advanced features
Insights
Which models are found to excel in dependency utilization according to the paper?
What is the significance of the new dataset introduced in the paper?
What is the primary purpose of REPOEXEC?
How does REPOEXEC evaluate code functionality?

REPOEXEC: Evaluate Code Generation with a Repository-Level Executable Benchmark

Nam Le Hai, Dung Manh Nguyen, Nghi D. Q. Bui·June 17, 2024

Summary

The paper presents REPOEXEC, a repository-level executable benchmark for evaluating code generation models, focusing on executability, functional correctness, and dependency management. It introduces a new dataset that enhances models' ability to handle dependencies and tests models' alignment with developer intent. Instruction-tuned models, like StarCoder and GPT-3.5, excel in dependency utilization, while pretrained models struggle. REPOEXEC assesses code functionality through metrics like Pass@k and Dependency Invocation Rate, and demonstrates the importance of comprehensive evaluation for real-world applications. The study also highlights the impact of context, fine-tuning, and multi-round debugging on model performance, with a focus on the CodeLLM community. The benchmark, dataset, and source code are publicly available for further research.
Mind map
Lessons learned and implications for model improvement
Community-driven debugging process (CodeLLM)
Challenges and limitations in dependency management
Dependency utilization strengths
Dependency Invocation Rate: Dependency management evaluation
Pass@k: Assessing functional correctness
Selection of instruction-tuned (e.g., StarCoder, GPT-3.5) and pretrained models
Focus on dependency management and developer intent
Description and creation process
Multi-Round Debugging
Pretrained Models
Instruction-Tuned Models
Evaluation Metrics
Code Generation Models
New Dataset
Addressing gaps in current evaluation methods
To evaluate code generation models on executability, functional correctness, and dependency management
Importance of executable benchmarks
Evolution of code generation models
Potential applications and implications for industry practices
Directions for further research and improvements
Public availability of benchmark, dataset, and source code
Recommendations for future model development
REPOEXEC benchmark's contribution to the field
Importance of comprehensive evaluation for real-world applications
Comparative analysis of model performance
Context and Fine-Tuning Impact
Performance Analysis
Data Preprocessing
Data Collection
Objective
Background
Future Work
Conclusion
Results and Discussion
Method
Introduction
Outline
Introduction
Background
Evolution of code generation models
Importance of executable benchmarks
Objective
To evaluate code generation models on executability, functional correctness, and dependency management
Addressing gaps in current evaluation methods
Method
Data Collection
New Dataset
Description and creation process
Focus on dependency management and developer intent
Code Generation Models
Selection of instruction-tuned (e.g., StarCoder, GPT-3.5) and pretrained models
Data Preprocessing
Cleaning and standardization of repository data
Handling of dependencies and context
Evaluation Metrics
Pass@k: Assessing functional correctness
Dependency Invocation Rate: Dependency management evaluation
Performance Analysis
Instruction-Tuned Models
Dependency utilization strengths
Pretrained Models
Challenges and limitations in dependency management
Context and Fine-Tuning Impact
Effect of context on model performance
Role of fine-tuning in improving executability
Multi-Round Debugging
Community-driven debugging process (CodeLLM)
Lessons learned and implications for model improvement
Results and Discussion
Comparative analysis of model performance
Importance of comprehensive evaluation for real-world applications
Conclusion
REPOEXEC benchmark's contribution to the field
Recommendations for future model development
Public availability of benchmark, dataset, and source code
Future Work
Directions for further research and improvements
Potential applications and implications for industry practices
Key findings
12

Paper digest

Q1. What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of effectively utilizing dependencies in code generation by introducing an instruction-tuning dataset to fine-tune language models (LLMs) for better dependency invocation accuracy and output correctness . This problem is not entirely new, as existing models, particularly pretrained LLMs, have struggled to efficiently reuse provided dependencies, leading to issues like technical debt and code smell . The paper's focus on enhancing the model's ability to reuse dependencies and ensure functional correctness through instruction-tuning is a novel approach to improving code generation performance .


Q2. What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis that instruction-tuning models in code generation, which utilize single-turn generation with instruction prompts, can enhance the model's ability to reuse dependencies, ensure functional correctness of the output, and improve efficiency compared to self-refinement models that involve multi-round debugging . The study focuses on evaluating how different language models leverage code dependencies when generating code, highlighting the impact of context size on the final results . Additionally, the paper introduces an instruction-tuning dataset that enhances dependency invocation accuracy and output correctness, even with limited context, offering potential for more efficient processing and reduced computational costs .


Q3. 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 innovative ideas, methods, and models in the field of code generation evaluation and improvement:

  1. Instruction-Tuning Dataset: The paper proposes an instruction-tuning dataset that focuses on fine-tuning base Large Language Models (LLMs) with a specific emphasis on code dependencies. This dataset is designed to enhance the accuracy of dependency invocation and output correctness, even with limited context .

  2. Instruction-Tuned Models: The study demonstrates that instruction-tuned models excel in utilizing provided dependencies and demonstrating debugging capabilities. These models show proficiency in leveraging given dependencies to provide correct solutions, addressing edge or corner cases that pretrained models may overlook. Despite their high Dependency Invocation Rate (DIR), instruction-tuned models may sometimes produce overly complex code, leading to incorrect solutions .

  3. Multi-Round Debugging for Code Generation: The paper explores the concept of self-refinement through multi-round debugging to enhance code generation performance. Models like WizardCoder, GPT3.5, and CodeLlama-13b-Python are subjected to error output logs and asked to fix errors through multiple rounds of debugging. This process results in significant improvements in pass rates and DIR, particularly for GPT-3.5 and WizardCoder, showcasing their high capacity for debugging and improvement in correctness .

  4. Impact of Context Size: The research highlights that the size of the context significantly impacts the final results of code generation models. It is noted that models fine-tuned with a small context can achieve results comparable to those obtained with a full context, offering potential for more efficient processing and reduced computational costs. This allows for the integration of various other types of context, enhancing the model's ability to reuse dependencies and ensure functional correctness of the output .

  5. Challenges with Existing Models: The paper identifies limitations in existing models, particularly in effectively reusing provided dependencies, potentially leading to technical debt and code smell issues. Pretrained LLMs are noted to excel in functional correctness but may struggle to reuse dependencies efficiently, sometimes duplicating the implementation from the given context, resulting in redundancy and code quality issues .

Overall, the paper introduces a comprehensive evaluation framework, novel datasets, and methodologies to enhance code generation models' ability to align with developer intent, improve functional correctness, and effectively utilize code dependencies for more reliable and applicable CodeLLMs in real-world scenarios. The paper "REPOEXEC: Evaluate Code Generation with a Repository-Level Executable Benchmark" introduces novel characteristics and advantages compared to previous methods in the field of code generation evaluation and improvement:

  1. Instruction-Tuning Dataset: The paper proposes an instruction-tuning dataset that focuses on fine-tuning base Large Language Models (LLMs) with a specific emphasis on code dependencies. This dataset enhances the accuracy of dependency invocation and output correctness, even with limited context .

  2. Improved Dependency Utilization: Instruction-tuned models demonstrate a higher capacity for utilizing given dependencies compared to foundation models. They excel in addressing edge or corner cases that foundation models may overlook, showcasing proficiency in leveraging dependencies for more accurate solutions .

  3. Multi-Round Debugging for Code Generation: The study explores self-refinement through multi-round debugging to enhance code generation performance. Models like WizardCoder, GPT3.5, and CodeLlama-13b-Python undergo multiple rounds of debugging, resulting in significant improvements in pass rates and Dependency Invocation Rate (DIR), particularly for GPT-3.5 and WizardCoder .

  4. Impact of Context Size: The research highlights that the size of the context significantly influences the final results of code generation models. Models fine-tuned with a small context can achieve results comparable to those obtained with a full context, offering potential for more efficient processing and reduced computational costs .

  5. Instruction-Tuning with Code Dependencies: The paper introduces an instruction-tuning dataset for fine-tuning base LLMs, enhancing dependency invocation accuracy and output correctness. This approach improves the model's ability to reuse dependencies and ensure functional correctness of the output, with significant improvements in Pass@1 and DIR after instruction tuning .

In summary, the paper's innovative methods, such as instruction-tuning datasets, multi-round debugging, and context size impact analysis, offer significant advancements in code generation evaluation by enhancing dependency utilization, debugging capabilities, and overall model performance compared to previous methods.


Q4. 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 related research papers exist in the field of code generation and understanding. Noteworthy researchers in this field include Yue Wang, Hung Le, Akhilesh Deepak Gotmare, Nghi DQ Bui, Junnan Li, Steven CH Hoi, Frank F Xu, Uri Alon, Graham Neubig, Vincent Josua Hellendoorn, Pengcheng Yin, Bowen Deng, Edgar Chen, Bogdan Vasilescu, Fengji Zhang, Bei Chen, Jacky Keung, Jin Liu, Daoguang Zan, Yi Mao, Jian-Guang Lou, Weizhu Chen, Kechi Zhang, Jia Li, Ge Li, Xianjie Shi, Zhi Jin, Tianyu Zheng, Ge Zhang, Tianhao Shen, Xueling Liu, Bill Yuchen Lin, Jie Fu, and Xiang Yue .

The key to the solution mentioned in the paper "REPOEXEC: Evaluate Code Generation with a Repository-Level Executable Benchmark" is the introduction of a novel benchmark called REPOEXEC. This benchmark focuses on three main aspects: executability, functional correctness through automated test case generation with high coverage rate, and carefully crafted cross-file contexts to accurately generate code. The paper explores a controlled scenario where developers specify necessary code dependencies, challenging the model to integrate these accurately. The experiments conducted show that while pretrained LLMs outperform instruction-tuned models in correctness, the latter excel in utilizing provided dependencies and demonstrating debugging capabilities. Additionally, the paper introduces a new instruction-tuned dataset that focuses on code dependencies, enhancing CodeLLMs' ability to leverage these dependencies effectively .


Q5. 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 focused on assessing the alignment of generated code with developer intent and its correctness . The evaluation metrics used in the experiments included Pass@k to measure the functional correctness of generation outputs and Dependency Invocation Rate (DIR) to assess the models' ability to utilize provided dependencies in accordance with human intent . The experiments involved evaluating various language models (LLMs) on REPOEXEC to understand how different models leverage code dependencies when generating code . The experiments also included instruction-tuning models to enhance dependency invocation accuracy and output correctness, even with limited context . Additionally, the experiments explored the effectiveness of self-refinement capabilities in enhancing generation performance through multi-round debugging . The experiments aimed to highlight the importance of maintaining high-quality test cases for evaluation purposes and to address limitations in effectively reusing provided dependencies in code generation models .


Q6. What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is called REPOEXEC . The study mentions that the authors will publicly release this dataset . This indicates that the code used in the evaluation is likely to be open source.


Q7. 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 strong support for the scientific hypotheses that needed verification. The study introduces REPOEXEC, a Python code generation benchmark that evaluates 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 pretrained and instruction-tuned models helps validate the hypothesis regarding the effectiveness of instruction-tuning in enhancing dependency invocation accuracy and output correctness .

Furthermore, the evaluation metrics used in the study, such as Pass@k and Dependency Invocation Rate (DIR), provide quantitative measures to assess the models' performance in generating code aligned with human intent and utilizing provided dependencies effectively . The results show improvements in Pass@1 and DIR after instruction tuning, indicating that the models were able to better incorporate dependencies into the generated code, supporting the hypothesis that instruction-tuning can enhance the model's ability to reuse dependencies and ensure functional correctness .

Moreover, the analysis of different context sizes, such as Full context, Medium context, and Short context, demonstrates how the size of the context significantly impacts the final results in code generation tasks . The findings reveal that utilizing Short context reduces the occurrence of empty function generation by the model and improves the models' ability in dependency calls, supporting the hypothesis that context size plays a crucial role in the effectiveness of code generation models .

In conclusion, the experiments and results presented in the paper provide substantial evidence to support the scientific hypotheses related to code generation, model performance, dependency utilization, and context size impact on code generation tasks. The comprehensive evaluation and analysis conducted in the study contribute to advancing the understanding of how different models leverage code dependencies and generate code aligned with developer intent .


Q8. What are the contributions of this paper?

The paper "REPOEXEC: Evaluate Code Generation with a Repository-Level Executable Benchmark" makes several key contributions in the field of code generation evaluation:

  • Introduction of REPOEXEC: The paper introduces REPOEXEC, a novel benchmark designed to evaluate code generation at the repository-level scale. It focuses on aspects such as executability, functional correctness through automated test case generation, and accurately generating code based on carefully crafted cross-file contexts .
  • Comparison of Pretrained and Instruction-Tuned Models: The study compares pretrained language models (LLMs) with instruction-tuned models in terms of functionality and alignment with developer intent. While pretrained LLMs excel in functional correctness, instruction-tuned models demonstrate proficiency in utilizing dependencies and debugging effectively .
  • 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 improves the model's ability to leverage dependencies effectively .
  • Improvements in Model Performance: The research demonstrates that instruction-tuning models show improvements in both Pass@1 and DIR metrics after tuning with the dataset. There is a slight increase in Pass@k for all models, while DIR scores significantly improve, reaching the highest scores compared to other models after tuning .
  • Efficiency and Cost Reduction: The study highlights that the use of small context in instruction-tuning models can lead to more efficient processing, reduced computational costs, and comparable results to those obtained with full context. This efficiency makes instruction-tuning models more practical for real-world applications .

Q9. What work can be continued in depth?

To delve deeper into the field of code generation and evaluation, several avenues for further exploration can be pursued based on the existing research and benchmarks:

  1. Enhancing Code Generation Models: Further research can focus on enhancing Code Large Language Models (CodeLLMs) to improve their ability to generate executable and functionally correct code at a repository-level scale . This could involve refining the models to better understand and integrate code dependencies accurately, leading to more reliable and applicable CodeLLMs for real-world scenarios.

  2. Automated Test Case Generation: There is potential to explore automated test case generation with high coverage rates to ensure the functional correctness of generated code . By developing methodologies or frameworks that can automatically generate comprehensive test cases, the reliability and robustness of code generation systems can be further improved.

  3. Cross-File Contexts in Code Generation: Research can focus on crafting cross-file contexts to accurately generate code, especially in scenarios where code spans multiple files or modules . By understanding and leveraging the relationships between different parts of a codebase, models can be trained to generate code that aligns with the developer's intent across various files.

  4. Instruction-Tuned Models and Code Dependencies: Investigating the performance of instruction-tuned models in utilizing provided dependencies and demonstrating debugging capabilities can be a valuable area of study . Comparing the effectiveness of instruction-tuned models versus pretrained LLMs in leveraging code dependencies could provide insights into the strengths and weaknesses of different model architectures.

  5. Dataset Expansion and Fine-Tuning: Expanding datasets and fine-tuning CodeLLMs on instruction-tuned datasets that focus on code dependencies could lead to improved model capabilities in leveraging dependencies effectively . By curating datasets that emphasize code dependencies, researchers can train models that are better equipped to understand and utilize external code resources.

In summary, further research in code generation and evaluation can focus on enhancing model capabilities, improving test case generation, exploring cross-file contexts, comparing different model architectures, and expanding datasets for fine-tuning models to better leverage code dependencies. These avenues can contribute to the development of more advanced and reliable code generation systems.

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5
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