Benchmarks and Metrics for Evaluations of Code Generation: A Critical Review

Debalina Ghosh Paul, Hong Zhu, Ian Bayley·June 18, 2024

Summary

This paper critically examines the evaluation of Large Language Models (LLMs) for code generation, focusing on the diversity of tasks (Description to Code, Code to Description, and Code to Code), with a particular emphasis on code generation. Models like Codex and Copilot are analyzed, with a distinction between general-purpose and specialized models. The paper highlights the lack of consensus in benchmarking and evaluation results, pointing out the need for better methods to assess fairness, significance, and the complexity of real-world tasks. Benchmarks, such as HumanEval, MBPP, and R-benchmark, are diverse and constructed from various sources, with varying levels of manual intervention and metrics like BLEU, ROUGE, and METEOR used for quantitative assessment. Challenges include benchmark diversity, test adequacy, and the development of usability-focused metrics. Future research should address these issues to improve the effectiveness and usability of LLMs in software development.

Paper digest

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

The paper "Benchmarks and Metrics for Evaluations of Code Generation: A Critical Review" aims to address the challenge of evaluating Large Language Models (LLMs) used for code generation tasks, focusing on the benchmarks and metrics employed in these evaluations . This paper delves into the existing research efforts and the issues surrounding the evaluation of LLMs as code generation tools, emphasizing the need for a comprehensive understanding of the current state of evaluation practices . While the use of LLMs for code generation tasks is not a new concept, the specific problem of effectively evaluating these models using appropriate benchmarks and metrics remains a significant and ongoing challenge in the field .


What scientific hypothesis does this paper seek to validate?

This paper aims to critically review existing work on testing and evaluating Large Language Models (LLMs) for code generation tasks, focusing on two key aspects: benchmarks and metrics used in the evaluations . The goal is to analyze the current state of evaluating LLMs as code generation tools and discuss future research directions .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Benchmarks and Metrics for Evaluations of Code Generation: A Critical Review" presents several new ideas, methods, and models related to evaluating Large Language Models (LLMs) for code generation tasks . The paper reviews existing work on testing and evaluating tools for code generation, focusing on benchmarks and metrics used in evaluations . It discusses the challenges in evaluating the performance of LLMs despite the significant research efforts in this area .

One key aspect highlighted in the paper is the importance of understanding the current state of the art in evaluating ML models as code generation tools . The review provides insights into various types of coding tasks that LLMs are applied to solve, such as Description to Code (D2C), Code to Description (C2D), and Code to Code (C2C) tasks . These tasks involve translating between natural language descriptions of programming problems and actual code in programming languages .

The paper discusses the emergence of Large Language Models (LLMs) as a recent breakthrough in the field of generating program code from natural language input . It outlines the different types of programming tasks that LLMs are designed to solve, including code generation (CG), document generation, code summarization, and comment generation . Special models like Codex and ChatGPT are highlighted as examples of ML models for code generation .

Furthermore, the paper provides a detailed comparison of various LLMs used for programming tasks, including their sizes, release years, benchmarks used for evaluation, and performance scores . It presents a performance comparison of different language models like GPT-NEO, GPT-J, Codex, Gemini-Ultra, CodeGen, SantaCoder, InCoder, and StarCoder, showcasing their capabilities in code generation tasks .

Overall, the paper offers a comprehensive analysis of the benchmarks, metrics, and performance evaluation methods used in assessing the effectiveness of Large Language Models for code generation tasks, shedding light on the current trends and challenges in this evolving field . The paper "Benchmarks and Metrics for Evaluations of Code Generation: A Critical Review" discusses several characteristics and advantages of evaluating Large Language Models (LLMs) for code generation tasks compared to previous methods .

One key advantage highlighted in the paper is the use of benchmarks and metrics to assess the performance of LLMs in generating program code from natural language input . These benchmarks and metrics provide objective and automatic evaluation methods, ensuring a standardized approach to measuring the effectiveness of LLMs in code generation tasks .

Furthermore, the paper emphasizes the importance of understanding the usability of generated code by users . Usability attributes such as understandability, structure, clarity, adaptability, and completeness are crucial for assessing the quality of the generated code . Evaluating the usability of LLM-generated code involves manual assessment based on quality attributes, task completion time, and the number of attempts required to obtain a satisfactory solution .

Additionally, the paper discusses the challenges associated with existing benchmarks, such as lack of diversity, skewed distribution, and limitations in judging correctness based on test results . It suggests the need to modify existing metrics to focus on measuring usability, which is more important than correctness for LLMs in code generation tasks .

Overall, the paper provides insights into the evolving landscape of evaluating LLMs for code generation tasks, highlighting the shift towards usability assessment, standardized benchmarks, and objective metrics to enhance the performance evaluation of Large Language Models in generating program code from natural language input .


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 evaluation. Noteworthy researchers in this area include Debalina Ghosh Paul, Hong Zhu, Ian Bayley, R. Li, P. Naik, S. Nelaballi, V.S. Pusuluri, D.K. Kim, J. Renzullo, P. Reiter, W. Weimer, S. Forrest, S. Ajorloo, D.M. Zapkus, A. Slotkien˙e, Gemini Team Google, B. Wang, A. Komatsuzaki, M. Chen, E. Nijkamp, L. B. Allal, D. Fried, D. Hendrycks, J. Austin, Y. Wan, A. Odeh, N. Odeh, A. S. Mohammed, J. L. Espejel, R. Balzer, H. Zhu, L. Jin, E. Dehaerne, S. Kulal, C.Y. Su, C. McMillan, L. Zhao, L. Zhang, S. Yan, X. Du, H. Yu, F. Cassano, Y. Lai, J. Liu, S. Iyer, I. Konstas, A. Cheung, L. Zettlemoyer, T. Miah, Y. Xie, J. Li, K. Papineni, S. Roukos, T. Ward, W.-J. Zhu, M. Popovi´c, N. Tran, S. Ren, A. Ziegler, F. F. Xu, B. Vasilescu, G. Neubig, M. Evtikhiev, E. Bogomolov, Y. Sokolov, T. Bryksin, and many others .

The key to the solution mentioned in the paper is to address the challenges in evaluating Large Language Models (LLMs) for code generation tasks. The paper critically reviews existing work on testing and evaluating tools for code generation, focusing on benchmarks and metrics used in evaluations. It discusses the need for further research to understand the current state of evaluating LLMs for code generation, emphasizing the importance of developing performance metrics that reflect the usability of ML models, validating these metrics, constructing versatile and feasible benchmarks, and automating the evaluation process .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the performance of Large Language Models (LLMs) for code generation tasks. The experiments focused on testing and evaluating these tools based on two key aspects: benchmarks and metrics . The paper critically reviewed existing work on testing and evaluation of LLMs, highlighting the challenges and discrepancies in the conclusions drawn from different evaluations . The experiments aimed to understand the current state of the art in evaluating LLMs for code generation tasks, discussing various benchmarks, metrics, and research directions in this domain .


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

The dataset used for quantitative evaluation in the context of code generation is "HumanEval+" . This dataset is utilized for evaluating the performance of Large Language Models (LLMs) in code generation tasks. Additionally, the code repository associated with this dataset is open source, as it is available on GitHub .


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 valuable insights into evaluating Large Language Models (LLMs) for code generation tasks, but there are some considerations to analyze the support for scientific hypotheses:

  1. Diversity and Adequacy of Benchmarks: The paper highlights that existing benchmarks may lack diversity and could be skewed towards certain types of questions . This limitation could impact the generalizability of the results and the ability to verify scientific hypotheses effectively.

  2. Judging Correctness and Usability: The paper discusses the challenges of judging correctness based on test results and emphasizes the importance of considering usability metrics for LLMs . This indicates that solely relying on correctness metrics may not fully support the scientific hypotheses related to the performance of LLMs in code generation tasks.

  3. Scenario-Based Evaluation: The paper points out that existing benchmarks do not support scenario-based evaluation effectively . Scenario-based evaluation is crucial for understanding how LLMs perform in real-world programming scenarios, which is essential for verifying scientific hypotheses regarding their practical utility.

  4. Performance Metrics and Usability: The paper suggests the need for developing performance metrics that reflect the usability of ML models, which is essential for verifying scientific hypotheses related to the effectiveness of LLMs in code generation tasks .

In conclusion, while the experiments and results in the paper contribute significantly to evaluating LLMs for code generation, there are important considerations such as benchmark diversity, usability metrics, and scenario-based evaluation that need to be addressed to provide stronger support for the scientific hypotheses that require verification in this domain.


What are the contributions of this paper?

The paper provides a critical review of existing work on testing and evaluating Large Language Models (LLMs) for code generation tasks, focusing on two key aspects: benchmarks and metrics used in evaluations . It discusses various types of coding tasks that LLMs have been applied to solve and summarizes the LLMs used or designed for coding problems . Additionally, the paper analyzes the current state of the art in the evaluation of ML models as code generation tools and suggests directions for future research .


What work can be continued in depth?

To further advance the evaluation of machine learning models for code generation, several areas warrant deeper exploration based on the critical review provided:

  • Development of Performance Metrics: There is a need to focus on creating performance metrics that truly reflect the usability of machine learning models (MLMs) as practical programming tools. This involves validating the metrics and ensuring they effectively capture the model's performance .
  • Construction of Versatile Benchmarks: Enhancing the construction of benchmarks to make them more versatile and feasible for evaluation is crucial. This includes addressing challenges related to benchmark structure, functionality, and the level of code generation tasks .
  • Automation of Evaluation: Exploring techniques and tools that enable the automation of evaluation processes is essential. This includes automating the extraction and processing of data from various sources to streamline the evaluation of MLMs .
  • Usability of MLMs: Investigating whether the evaluations and comparisons of MLMs are fair and if the differences observed are significant is important. Understanding how well the performance evaluations reflect the practical usability of MLMs in programming tasks is a key area for further research .

Introduction
Background
Emergence of Large Language Models in code generation
Importance of code generation tasks (Description to Code, Code to Description, Code to Code)
Objective
To critically assess LLM evaluation for code generation
Emphasis on diversity of tasks and models (general-purpose vs. specialized)
Highlighting the need for improved benchmarking methods
Method
Data Collection
Analysis of models: Codex, Copilot, and others
Review of benchmark datasets (HumanEval, MBPP, R-benchmark)
Examination of manual intervention levels in benchmarks
Data Preprocessing
Assessment of benchmark diversity and construction methods
Metrics used: BLEU, ROUGE, METEOR, and others
Challenges
Benchmark Diversity
Inconsistencies in benchmark tasks and composition
Test Adequacy
Ensuring benchmarks represent real-world complexity
Usability Metrics
Development of metrics focused on practical usability in software development
Evaluation Criteria
Fairness and Significance
Assessing model performance in a fair and meaningful context
Addressing biases and generalizability
Real-World Task Complexity
Evaluating models' ability to handle complex, real-world coding scenarios
Current Benchmarks and Limitations
HumanEval: Human-composed tasks and evaluation
MBPP: Multi-programming benchmark with varying difficulty
R-benchmark: Code-to-code translation challenges
Future Research Directions
Standardization of benchmarking practices
Development of more comprehensive and task-specific evaluation methods
Integration of usability and practicality in model assessment
Conclusion
Recap of the importance of improved evaluation for LLMs in code generation
Call to action for the research community to address current challenges
Basic info
papers
software engineering
artificial intelligence
Advanced features
Insights
What are some of the challenges and benchmarks mentioned in the paper that need to be addressed in future research for better LLM performance in software development?
What are the primary tasks that Large Language Models (LLMs) for code generation are evaluated on?
Which models are specifically mentioned for analysis in the paper, and what is the distinction between general-purpose and specialized models?
What are the main concerns raised regarding benchmarking and evaluation of LLMs for code generation?

Benchmarks and Metrics for Evaluations of Code Generation: A Critical Review

Debalina Ghosh Paul, Hong Zhu, Ian Bayley·June 18, 2024

Summary

This paper critically examines the evaluation of Large Language Models (LLMs) for code generation, focusing on the diversity of tasks (Description to Code, Code to Description, and Code to Code), with a particular emphasis on code generation. Models like Codex and Copilot are analyzed, with a distinction between general-purpose and specialized models. The paper highlights the lack of consensus in benchmarking and evaluation results, pointing out the need for better methods to assess fairness, significance, and the complexity of real-world tasks. Benchmarks, such as HumanEval, MBPP, and R-benchmark, are diverse and constructed from various sources, with varying levels of manual intervention and metrics like BLEU, ROUGE, and METEOR used for quantitative assessment. Challenges include benchmark diversity, test adequacy, and the development of usability-focused metrics. Future research should address these issues to improve the effectiveness and usability of LLMs in software development.
Mind map
Development of metrics focused on practical usability in software development
Usability Metrics
Ensuring benchmarks represent real-world complexity
Test Adequacy
Inconsistencies in benchmark tasks and composition
Benchmark Diversity
Evaluating models' ability to handle complex, real-world coding scenarios
Addressing biases and generalizability
Assessing model performance in a fair and meaningful context
Challenges
Examination of manual intervention levels in benchmarks
Review of benchmark datasets (HumanEval, MBPP, R-benchmark)
Analysis of models: Codex, Copilot, and others
Highlighting the need for improved benchmarking methods
Emphasis on diversity of tasks and models (general-purpose vs. specialized)
To critically assess LLM evaluation for code generation
Importance of code generation tasks (Description to Code, Code to Description, Code to Code)
Emergence of Large Language Models in code generation
Call to action for the research community to address current challenges
Recap of the importance of improved evaluation for LLMs in code generation
Integration of usability and practicality in model assessment
Development of more comprehensive and task-specific evaluation methods
Standardization of benchmarking practices
R-benchmark: Code-to-code translation challenges
MBPP: Multi-programming benchmark with varying difficulty
HumanEval: Human-composed tasks and evaluation
Real-World Task Complexity
Fairness and Significance
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Future Research Directions
Current Benchmarks and Limitations
Evaluation Criteria
Method
Introduction
Outline
Introduction
Background
Emergence of Large Language Models in code generation
Importance of code generation tasks (Description to Code, Code to Description, Code to Code)
Objective
To critically assess LLM evaluation for code generation
Emphasis on diversity of tasks and models (general-purpose vs. specialized)
Highlighting the need for improved benchmarking methods
Method
Data Collection
Analysis of models: Codex, Copilot, and others
Review of benchmark datasets (HumanEval, MBPP, R-benchmark)
Examination of manual intervention levels in benchmarks
Data Preprocessing
Assessment of benchmark diversity and construction methods
Metrics used: BLEU, ROUGE, METEOR, and others
Challenges
Benchmark Diversity
Inconsistencies in benchmark tasks and composition
Test Adequacy
Ensuring benchmarks represent real-world complexity
Usability Metrics
Development of metrics focused on practical usability in software development
Evaluation Criteria
Fairness and Significance
Assessing model performance in a fair and meaningful context
Addressing biases and generalizability
Real-World Task Complexity
Evaluating models' ability to handle complex, real-world coding scenarios
Current Benchmarks and Limitations
HumanEval: Human-composed tasks and evaluation
MBPP: Multi-programming benchmark with varying difficulty
R-benchmark: Code-to-code translation challenges
Future Research Directions
Standardization of benchmarking practices
Development of more comprehensive and task-specific evaluation methods
Integration of usability and practicality in model assessment
Conclusion
Recap of the importance of improved evaluation for LLMs in code generation
Call to action for the research community to address current challenges

Paper digest

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

The paper "Benchmarks and Metrics for Evaluations of Code Generation: A Critical Review" aims to address the challenge of evaluating Large Language Models (LLMs) used for code generation tasks, focusing on the benchmarks and metrics employed in these evaluations . This paper delves into the existing research efforts and the issues surrounding the evaluation of LLMs as code generation tools, emphasizing the need for a comprehensive understanding of the current state of evaluation practices . While the use of LLMs for code generation tasks is not a new concept, the specific problem of effectively evaluating these models using appropriate benchmarks and metrics remains a significant and ongoing challenge in the field .


What scientific hypothesis does this paper seek to validate?

This paper aims to critically review existing work on testing and evaluating Large Language Models (LLMs) for code generation tasks, focusing on two key aspects: benchmarks and metrics used in the evaluations . The goal is to analyze the current state of evaluating LLMs as code generation tools and discuss future research directions .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Benchmarks and Metrics for Evaluations of Code Generation: A Critical Review" presents several new ideas, methods, and models related to evaluating Large Language Models (LLMs) for code generation tasks . The paper reviews existing work on testing and evaluating tools for code generation, focusing on benchmarks and metrics used in evaluations . It discusses the challenges in evaluating the performance of LLMs despite the significant research efforts in this area .

One key aspect highlighted in the paper is the importance of understanding the current state of the art in evaluating ML models as code generation tools . The review provides insights into various types of coding tasks that LLMs are applied to solve, such as Description to Code (D2C), Code to Description (C2D), and Code to Code (C2C) tasks . These tasks involve translating between natural language descriptions of programming problems and actual code in programming languages .

The paper discusses the emergence of Large Language Models (LLMs) as a recent breakthrough in the field of generating program code from natural language input . It outlines the different types of programming tasks that LLMs are designed to solve, including code generation (CG), document generation, code summarization, and comment generation . Special models like Codex and ChatGPT are highlighted as examples of ML models for code generation .

Furthermore, the paper provides a detailed comparison of various LLMs used for programming tasks, including their sizes, release years, benchmarks used for evaluation, and performance scores . It presents a performance comparison of different language models like GPT-NEO, GPT-J, Codex, Gemini-Ultra, CodeGen, SantaCoder, InCoder, and StarCoder, showcasing their capabilities in code generation tasks .

Overall, the paper offers a comprehensive analysis of the benchmarks, metrics, and performance evaluation methods used in assessing the effectiveness of Large Language Models for code generation tasks, shedding light on the current trends and challenges in this evolving field . The paper "Benchmarks and Metrics for Evaluations of Code Generation: A Critical Review" discusses several characteristics and advantages of evaluating Large Language Models (LLMs) for code generation tasks compared to previous methods .

One key advantage highlighted in the paper is the use of benchmarks and metrics to assess the performance of LLMs in generating program code from natural language input . These benchmarks and metrics provide objective and automatic evaluation methods, ensuring a standardized approach to measuring the effectiveness of LLMs in code generation tasks .

Furthermore, the paper emphasizes the importance of understanding the usability of generated code by users . Usability attributes such as understandability, structure, clarity, adaptability, and completeness are crucial for assessing the quality of the generated code . Evaluating the usability of LLM-generated code involves manual assessment based on quality attributes, task completion time, and the number of attempts required to obtain a satisfactory solution .

Additionally, the paper discusses the challenges associated with existing benchmarks, such as lack of diversity, skewed distribution, and limitations in judging correctness based on test results . It suggests the need to modify existing metrics to focus on measuring usability, which is more important than correctness for LLMs in code generation tasks .

Overall, the paper provides insights into the evolving landscape of evaluating LLMs for code generation tasks, highlighting the shift towards usability assessment, standardized benchmarks, and objective metrics to enhance the performance evaluation of Large Language Models in generating program code from natural language input .


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 evaluation. Noteworthy researchers in this area include Debalina Ghosh Paul, Hong Zhu, Ian Bayley, R. Li, P. Naik, S. Nelaballi, V.S. Pusuluri, D.K. Kim, J. Renzullo, P. Reiter, W. Weimer, S. Forrest, S. Ajorloo, D.M. Zapkus, A. Slotkien˙e, Gemini Team Google, B. Wang, A. Komatsuzaki, M. Chen, E. Nijkamp, L. B. Allal, D. Fried, D. Hendrycks, J. Austin, Y. Wan, A. Odeh, N. Odeh, A. S. Mohammed, J. L. Espejel, R. Balzer, H. Zhu, L. Jin, E. Dehaerne, S. Kulal, C.Y. Su, C. McMillan, L. Zhao, L. Zhang, S. Yan, X. Du, H. Yu, F. Cassano, Y. Lai, J. Liu, S. Iyer, I. Konstas, A. Cheung, L. Zettlemoyer, T. Miah, Y. Xie, J. Li, K. Papineni, S. Roukos, T. Ward, W.-J. Zhu, M. Popovi´c, N. Tran, S. Ren, A. Ziegler, F. F. Xu, B. Vasilescu, G. Neubig, M. Evtikhiev, E. Bogomolov, Y. Sokolov, T. Bryksin, and many others .

The key to the solution mentioned in the paper is to address the challenges in evaluating Large Language Models (LLMs) for code generation tasks. The paper critically reviews existing work on testing and evaluating tools for code generation, focusing on benchmarks and metrics used in evaluations. It discusses the need for further research to understand the current state of evaluating LLMs for code generation, emphasizing the importance of developing performance metrics that reflect the usability of ML models, validating these metrics, constructing versatile and feasible benchmarks, and automating the evaluation process .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the performance of Large Language Models (LLMs) for code generation tasks. The experiments focused on testing and evaluating these tools based on two key aspects: benchmarks and metrics . The paper critically reviewed existing work on testing and evaluation of LLMs, highlighting the challenges and discrepancies in the conclusions drawn from different evaluations . The experiments aimed to understand the current state of the art in evaluating LLMs for code generation tasks, discussing various benchmarks, metrics, and research directions in this domain .


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

The dataset used for quantitative evaluation in the context of code generation is "HumanEval+" . This dataset is utilized for evaluating the performance of Large Language Models (LLMs) in code generation tasks. Additionally, the code repository associated with this dataset is open source, as it is available on GitHub .


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 valuable insights into evaluating Large Language Models (LLMs) for code generation tasks, but there are some considerations to analyze the support for scientific hypotheses:

  1. Diversity and Adequacy of Benchmarks: The paper highlights that existing benchmarks may lack diversity and could be skewed towards certain types of questions . This limitation could impact the generalizability of the results and the ability to verify scientific hypotheses effectively.

  2. Judging Correctness and Usability: The paper discusses the challenges of judging correctness based on test results and emphasizes the importance of considering usability metrics for LLMs . This indicates that solely relying on correctness metrics may not fully support the scientific hypotheses related to the performance of LLMs in code generation tasks.

  3. Scenario-Based Evaluation: The paper points out that existing benchmarks do not support scenario-based evaluation effectively . Scenario-based evaluation is crucial for understanding how LLMs perform in real-world programming scenarios, which is essential for verifying scientific hypotheses regarding their practical utility.

  4. Performance Metrics and Usability: The paper suggests the need for developing performance metrics that reflect the usability of ML models, which is essential for verifying scientific hypotheses related to the effectiveness of LLMs in code generation tasks .

In conclusion, while the experiments and results in the paper contribute significantly to evaluating LLMs for code generation, there are important considerations such as benchmark diversity, usability metrics, and scenario-based evaluation that need to be addressed to provide stronger support for the scientific hypotheses that require verification in this domain.


What are the contributions of this paper?

The paper provides a critical review of existing work on testing and evaluating Large Language Models (LLMs) for code generation tasks, focusing on two key aspects: benchmarks and metrics used in evaluations . It discusses various types of coding tasks that LLMs have been applied to solve and summarizes the LLMs used or designed for coding problems . Additionally, the paper analyzes the current state of the art in the evaluation of ML models as code generation tools and suggests directions for future research .


What work can be continued in depth?

To further advance the evaluation of machine learning models for code generation, several areas warrant deeper exploration based on the critical review provided:

  • Development of Performance Metrics: There is a need to focus on creating performance metrics that truly reflect the usability of machine learning models (MLMs) as practical programming tools. This involves validating the metrics and ensuring they effectively capture the model's performance .
  • Construction of Versatile Benchmarks: Enhancing the construction of benchmarks to make them more versatile and feasible for evaluation is crucial. This includes addressing challenges related to benchmark structure, functionality, and the level of code generation tasks .
  • Automation of Evaluation: Exploring techniques and tools that enable the automation of evaluation processes is essential. This includes automating the extraction and processing of data from various sources to streamline the evaluation of MLMs .
  • Usability of MLMs: Investigating whether the evaluations and comparisons of MLMs are fair and if the differences observed are significant is important. Understanding how well the performance evaluations reflect the practical usability of MLMs in programming tasks is a key area for further research .
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