Long Code Arena: a Set of Benchmarks for Long-Context Code Models

Egor Bogomolov, Aleksandra Eliseeva, Timur Galimzyanov, Evgeniy Glukhov, Anton Shapkin, Maria Tigina, Yaroslav Golubev, Alexander Kovrigin, Arie van Deursen, Maliheh Izadi, Timofey Bryksin·June 17, 2024

Summary

Long Code Arena is a suite of six code processing benchmarks, addressing the need for project-wide context in ML4SE tasks. The suite includes code generation, CI build repair, code completion, commit message generation, bug localization, and module summarization. Datasets are manually curated, with open-source baselines, aiming to promote research on long-context code understanding and practical applications. Benchmarks involve tasks like generating code from natural language instructions, fixing failing builds, and summarizing code modules. The datasets are primarily focused on Python, with varying levels of complexity and context. The paper highlights the importance of evaluating models on real-world tasks and the potential for large language models like GPT-4 to perform well, but also acknowledges the need for further improvement. The Long Code Arena platform encourages collaboration and contributes to the advancement of AI for software engineering.

Paper digest

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

The paper aims to address the bug localization task within the Long Code Arena, which involves identifying the specific files in a repository that need to be modified to address reported bugs based on bug descriptions and repository snapshots . This problem is not entirely new but requires a separate evaluation to understand different approaches' efficiency in precisely locating bugs within large code bases .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis related to the bug localization benchmark within the Long Code Arena. The scientific hypothesis being investigated is the evaluation of models' capabilities in locating files that need to be modified based on a bug description. The dataset includes real bug issues along with corresponding pull requests that fix them. The model under evaluation takes a bug description and the repository state before the fix to output the list of files requiring changes .


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

The paper introduces several novel ideas, methods, and models in the field of long-context code models:

  • Unlimiformer: The paper presents Unlimiformer, a long-range transformer model with unlimited length input, designed to enhance the performance of language models .
  • CompScore Metric: A new metric called CompScore is introduced to evaluate the quality of generated documentation by assessing which documentation better explains and fits the code. This metric utilizes a large language model (LLM) as an assessor and calculates the probability that the generated documentation is superior by averaging the results of two queries .
  • Mistral-7B-Instruct-v0.2: The paper utilizes Mistral-7B-Instruct-v0.2 as an LLM assessor for experiments, truncating relevant code up to 6,000 tokens in the prompt for metric computation .
  • Evaluation of Large Language Models: The paper evaluates large language models trained on code, focusing on the efficiency and effectiveness of these models in code-related tasks .
  • FlashAttention-2: The paper discusses FlashAttention-2, a model that aims to improve attention mechanisms in transformers for better parallelism and work partitioning .
  • Hyena Hierarchy: The paper introduces the Hyena hierarchy model, which contributes to the development of larger convolutional language models .
  • RepoCoder: The paper presents RepoCoder, a model for repository-level code completion through iterative retrieval and generation .

These contributions showcase advancements in the development and evaluation of long-context code models, aiming to enhance the performance and capabilities of language models in code-related tasks. The paper introduces several novel characteristics and advantages compared to previous methods in the field of long-context code models:

  • Unlimiformer Model: The paper presents the Unlimiformer model, which is a long-range transformer model designed to handle unlimited length input, aiming to enhance the performance of language models .
  • CompScore Metric: A new metric called CompScore is introduced to evaluate the quality of generated documentation by utilizing large language models (LLMs) as scalable proxies for human assessors. This metric assesses which documentation better explains and fits the code, addressing the limitations of n-gram-based metrics like ChrF for discriminating long files .
  • Mistral-7B-Instruct-v0.2: The paper utilizes Mistral-7B-Instruct-v0.2 as an LLM assessor for experiments, truncating relevant code up to 6,000 tokens in the prompt for metric computation, showcasing advancements in model evaluation .
  • Evaluation of Large Language Models: The paper evaluates large language models trained on code, focusing on their efficiency and effectiveness in code-related tasks, contributing to the advancement of model evaluation techniques .
  • FlashAttention-2 and Hyena Hierarchy Models: The paper discusses FlashAttention-2, a model aimed at improving attention mechanisms in transformers for better parallelism and work partitioning, along with the introduction of the Hyena hierarchy model, contributing to the development of larger convolutional language models .
  • RepoCoder Model: The paper presents RepoCoder, a model designed for repository-level code completion through iterative retrieval and generation, offering a new approach to code completion tasks .

These advancements in long-context code models demonstrate improvements in model performance, evaluation metrics, attention mechanisms, and code completion techniques, enhancing the capabilities and efficiency of language models in code-related tasks.


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 long-context code models. Noteworthy researchers in this area include Amanda Bertsch, Uri Alon, Graham Neubig, Matthew Gormley, Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, and many others . One key solution mentioned in the papers is the development of long-range transformers with unlimited length input, such as the Unlimiformer model . This solution aims to enhance the performance of language models on long-context code tasks by allowing them to process extensive input sequences effectively.


How were the experiments in the paper designed?

The experiments in the paper were designed as follows:

  • The dataset was created by the JetBrains Research team for the purpose of evaluating how well machine learning models can utilize data from an entire software project for code generation tasks .
  • The data collection process involved using the GitHub API to collect the initial list of repositories, followed by manual verification and assessment by the authors of the paper .
  • The dataset construction took place between October 2023 and January 2024 .
  • The dataset consists of 150 samples from 62 libraries, with each sample heavily relying on the APIs of the respective project .
  • The experiments involved assessing the quality of models in the library-based code generation task by developing and evaluating multiple baseline solutions and proposing metrics such as ChrF and API Recall for quality assessment .
  • Various language models were evaluated in two setups, including proprietary models like GPT-3.5-turbo and GPT-4, as well as open-source models like CodeLlama-7B, CodeLlama-70B, Mistral-7B, and Mixtral-8x7B .
  • The experiments aimed to measure the similarity between generated code and human-written code using metrics like ChrF and API Recall, which assess the usage of library-specific methods and classes in the generated code .
  • The dataset was used to evaluate the quality of models in tasks like library-based code generation and module summarization, introducing metrics like CompScore to assess the generated documentation quality .

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

The dataset used for quantitative evaluation in the study is the CommitChronicle dataset . The code in the dataset was collected from openly available GitHub repositories with permissive licenses, ensuring that the data found was intended to be shared freely . The dataset consists of code and artifacts written by human users on GitHub, but the focus is on the code itself rather than personal information or authorship details . The dataset is publicly available on the internet and can be accessed through a DOI at the HuggingFace Hub . The terms of use require that any research conducted using this dataset makes resulting papers available as open access, aligning with GitHub's requirements .


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 verification. The paper outlines a detailed methodology for evaluating models on repository-level long-context real-life tasks, specifically focusing on the CI builds repair benchmark to test models in fixing real-life issues in continuous integration . The dataset includes real issues describing bugs and their respective pull requests, aiming to evaluate models' abilities in locating files that need to be changed given a bug description . This structured approach ensures a rigorous evaluation of the models' performance in real-world scenarios.

Moreover, the paper describes the use of a new metric called CompScore to assess the quality of generated documentation by feeding relevant code and two versions of documentation to an assessor LLM . This metric calculates the probability that the generated documentation is superior, providing a quantitative measure to evaluate the models' performance objectively. Additionally, the experiments involve running several LLMs on a collected module summarization dataset with different lengths of relevant code context, further enhancing the robustness of the evaluation process .

Furthermore, the paper discusses the maintenance plan for the dataset, indicating that it will be extended to include more languages and samples over time . This continuous improvement and expansion of the dataset ensure that the experiments and results remain relevant and up-to-date, supporting ongoing scientific inquiry and hypothesis verification. Overall, the comprehensive methodology, detailed evaluation metrics, and planned dataset enhancements demonstrate a strong foundation for verifying scientific hypotheses in the field of code models and generation.


What are the contributions of this paper?

The paper provides several key contributions:

  • It introduces Unlimiformer, a model that extends transformers to handle unlimited length input .
  • It presents research on improving language models by retrieving from trillions of tokens .
  • The paper evaluates large language models trained on code, exploring their potential as an alternative to human evaluations .
  • It discusses the feature of commit message generation in GitHub Copilot and JetBrains IDEs .
  • The paper also delves into the evaluation of large language models for code generation, specifically focusing on the correctness of code generated by ChatPGT .

What work can be continued in depth?

The Long Code Arena project aims to stimulate research in ML-based solutions for software engineering tasks by providing a suite of benchmarks that require considering complex contexts . Future work on the Long Code Arena includes extending datasets to other programming languages, collecting data for fine-tuning models for specific tasks, and evaluating more models on the benchmarks . Researchers are encouraged to advance the field of ML-enabled software engineering by leveraging the Long Code Arena benchmarks to address tasks such as code generation, repair, completion, and summarization .

To further advance the field, researchers can focus on enhancing models' capabilities to process long-context windows efficiently, as supported context sizes have significantly increased in recent years . Additionally, exploring tasks beyond single-file contexts, such as project-wide context tasks, can help bridge the gap in benchmarks for code processing that require a broader scope . This expansion can lead to more comprehensive evaluations and advancements in ML-enabled software engineering .

Continued efforts in developing benchmarks for tasks like library-based code generation, CI builds repair, project-level code completion, commit message generation, bug localization, and module summarization can provide valuable insights into the performance of models in handling real-life software engineering challenges . By designing tasks that necessitate utilizing information from project modules or entire repositories, researchers can push the boundaries of ML4SE models and enhance their practical applicability .

Moreover, ongoing work on updating datasets with new instances, correcting labeling errors, and removing obsolete data points is crucial to maintaining the relevance and quality of the benchmarks over time . Researchers can contribute to the Long Code Arena project by extending, augmenting, or building on the existing datasets, thereby fostering collaboration and advancements in ML-enabled software engineering .


Introduction
Background
[ ] Emergence of ML4SE tasks and the need for project-wide context
[ ] Current gaps in existing benchmarks
Objective
[ ] To address the need for long-context code understanding
[ ] To promote research on practical applications in software engineering
[ ] To evaluate models on real-world tasks
Method
Data Collection
Code Generation
[ ] Natural language instructions to code dataset curation
[ ] Selection of open-source projects
CI Build Repair
[ ] Failing build logs and required fixes
[ ] Manual curation of repair tasks
Code Completion
[ ] Large-scale Python code snippets with context
[ ] Data collection from diverse repositories
Data Preprocessing
[ ] Cleaning and standardization of code snippets
[ ] Handling variable and function names
[ ] Ensuring diverse context lengths
[ ] Splitting data into training, validation, and test sets
Benchmarks
Code Generation
Task description
Evaluation metrics
Baseline models and performance
CI Build Repair
Repair tasks and scenarios
Success rate and practical impact
Model performance analysis
Code Completion
Completion accuracy with varying context
Comparison with human performance
Commit Message Generation
Criteria for effective commit messages
Model-generated messages vs. human-written
Bug Localization
Identifying root causes from code and logs
Precision and recall metrics
Module Summarization
Summarizing code modules for documentation
Human evaluation and model effectiveness
Large Language Models and Potential
[ ] GPT-4 and other models' performance on the suite
[ ] Limitations and areas for improvement
[ ] Future directions in AI for software engineering
Collaboration and Platform
[ ] Long Code Arena as a community resource
[ ] Encouraging research and model comparisons
[ ] Contribution guidelines and best practices
Conclusion
[ ] Importance of Long Code Arena in advancing AI for software engineering
[ ] Summary of key findings and implications
[ ] Future research directions and challenges
Basic info
papers
information retrieval
software engineering
machine learning
artificial intelligence
Advanced features
Insights
What are the primary tasks in the Long Code Arena suite?
What is Long Code Arena?
Why are the manually curated datasets in Long Code Arena significant?
What are the six code processing benchmarks included in Long Code Arena?

Long Code Arena: a Set of Benchmarks for Long-Context Code Models

Egor Bogomolov, Aleksandra Eliseeva, Timur Galimzyanov, Evgeniy Glukhov, Anton Shapkin, Maria Tigina, Yaroslav Golubev, Alexander Kovrigin, Arie van Deursen, Maliheh Izadi, Timofey Bryksin·June 17, 2024

Summary

Long Code Arena is a suite of six code processing benchmarks, addressing the need for project-wide context in ML4SE tasks. The suite includes code generation, CI build repair, code completion, commit message generation, bug localization, and module summarization. Datasets are manually curated, with open-source baselines, aiming to promote research on long-context code understanding and practical applications. Benchmarks involve tasks like generating code from natural language instructions, fixing failing builds, and summarizing code modules. The datasets are primarily focused on Python, with varying levels of complexity and context. The paper highlights the importance of evaluating models on real-world tasks and the potential for large language models like GPT-4 to perform well, but also acknowledges the need for further improvement. The Long Code Arena platform encourages collaboration and contributes to the advancement of AI for software engineering.
Mind map
Data collection from diverse repositories
Large-scale Python code snippets with context
Manual curation of repair tasks
Failing build logs and required fixes
Selection of open-source projects
Natural language instructions to code dataset curation
Human evaluation and model effectiveness
Summarizing code modules for documentation
Precision and recall metrics
Identifying root causes from code and logs
Model-generated messages vs. human-written
Criteria for effective commit messages
Comparison with human performance
Completion accuracy with varying context
Model performance analysis
Success rate and practical impact
Repair tasks and scenarios
Baseline models and performance
Evaluation metrics
Task description
Splitting data into training, validation, and test sets
Ensuring diverse context lengths
Handling variable and function names
Cleaning and standardization of code snippets
Code Completion
CI Build Repair
Code Generation
To evaluate models on real-world tasks
To promote research on practical applications in software engineering
To address the need for long-context code understanding
Current gaps in existing benchmarks
Emergence of ML4SE tasks and the need for project-wide context
Future research directions and challenges
Summary of key findings and implications
Importance of Long Code Arena in advancing AI for software engineering
Contribution guidelines and best practices
Encouraging research and model comparisons
Long Code Arena as a community resource
Future directions in AI for software engineering
Limitations and areas for improvement
GPT-4 and other models' performance on the suite
Module Summarization
Bug Localization
Commit Message Generation
Code Completion
CI Build Repair
Code Generation
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Collaboration and Platform
Large Language Models and Potential
Benchmarks
Method
Introduction
Outline
Introduction
Background
[ ] Emergence of ML4SE tasks and the need for project-wide context
[ ] Current gaps in existing benchmarks
Objective
[ ] To address the need for long-context code understanding
[ ] To promote research on practical applications in software engineering
[ ] To evaluate models on real-world tasks
Method
Data Collection
Code Generation
[ ] Natural language instructions to code dataset curation
[ ] Selection of open-source projects
CI Build Repair
[ ] Failing build logs and required fixes
[ ] Manual curation of repair tasks
Code Completion
[ ] Large-scale Python code snippets with context
[ ] Data collection from diverse repositories
Data Preprocessing
[ ] Cleaning and standardization of code snippets
[ ] Handling variable and function names
[ ] Ensuring diverse context lengths
[ ] Splitting data into training, validation, and test sets
Benchmarks
Code Generation
Task description
Evaluation metrics
Baseline models and performance
CI Build Repair
Repair tasks and scenarios
Success rate and practical impact
Model performance analysis
Code Completion
Completion accuracy with varying context
Comparison with human performance
Commit Message Generation
Criteria for effective commit messages
Model-generated messages vs. human-written
Bug Localization
Identifying root causes from code and logs
Precision and recall metrics
Module Summarization
Summarizing code modules for documentation
Human evaluation and model effectiveness
Large Language Models and Potential
[ ] GPT-4 and other models' performance on the suite
[ ] Limitations and areas for improvement
[ ] Future directions in AI for software engineering
Collaboration and Platform
[ ] Long Code Arena as a community resource
[ ] Encouraging research and model comparisons
[ ] Contribution guidelines and best practices
Conclusion
[ ] Importance of Long Code Arena in advancing AI for software engineering
[ ] Summary of key findings and implications
[ ] Future research directions and challenges

Paper digest

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

The paper aims to address the bug localization task within the Long Code Arena, which involves identifying the specific files in a repository that need to be modified to address reported bugs based on bug descriptions and repository snapshots . This problem is not entirely new but requires a separate evaluation to understand different approaches' efficiency in precisely locating bugs within large code bases .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis related to the bug localization benchmark within the Long Code Arena. The scientific hypothesis being investigated is the evaluation of models' capabilities in locating files that need to be modified based on a bug description. The dataset includes real bug issues along with corresponding pull requests that fix them. The model under evaluation takes a bug description and the repository state before the fix to output the list of files requiring changes .


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

The paper introduces several novel ideas, methods, and models in the field of long-context code models:

  • Unlimiformer: The paper presents Unlimiformer, a long-range transformer model with unlimited length input, designed to enhance the performance of language models .
  • CompScore Metric: A new metric called CompScore is introduced to evaluate the quality of generated documentation by assessing which documentation better explains and fits the code. This metric utilizes a large language model (LLM) as an assessor and calculates the probability that the generated documentation is superior by averaging the results of two queries .
  • Mistral-7B-Instruct-v0.2: The paper utilizes Mistral-7B-Instruct-v0.2 as an LLM assessor for experiments, truncating relevant code up to 6,000 tokens in the prompt for metric computation .
  • Evaluation of Large Language Models: The paper evaluates large language models trained on code, focusing on the efficiency and effectiveness of these models in code-related tasks .
  • FlashAttention-2: The paper discusses FlashAttention-2, a model that aims to improve attention mechanisms in transformers for better parallelism and work partitioning .
  • Hyena Hierarchy: The paper introduces the Hyena hierarchy model, which contributes to the development of larger convolutional language models .
  • RepoCoder: The paper presents RepoCoder, a model for repository-level code completion through iterative retrieval and generation .

These contributions showcase advancements in the development and evaluation of long-context code models, aiming to enhance the performance and capabilities of language models in code-related tasks. The paper introduces several novel characteristics and advantages compared to previous methods in the field of long-context code models:

  • Unlimiformer Model: The paper presents the Unlimiformer model, which is a long-range transformer model designed to handle unlimited length input, aiming to enhance the performance of language models .
  • CompScore Metric: A new metric called CompScore is introduced to evaluate the quality of generated documentation by utilizing large language models (LLMs) as scalable proxies for human assessors. This metric assesses which documentation better explains and fits the code, addressing the limitations of n-gram-based metrics like ChrF for discriminating long files .
  • Mistral-7B-Instruct-v0.2: The paper utilizes Mistral-7B-Instruct-v0.2 as an LLM assessor for experiments, truncating relevant code up to 6,000 tokens in the prompt for metric computation, showcasing advancements in model evaluation .
  • Evaluation of Large Language Models: The paper evaluates large language models trained on code, focusing on their efficiency and effectiveness in code-related tasks, contributing to the advancement of model evaluation techniques .
  • FlashAttention-2 and Hyena Hierarchy Models: The paper discusses FlashAttention-2, a model aimed at improving attention mechanisms in transformers for better parallelism and work partitioning, along with the introduction of the Hyena hierarchy model, contributing to the development of larger convolutional language models .
  • RepoCoder Model: The paper presents RepoCoder, a model designed for repository-level code completion through iterative retrieval and generation, offering a new approach to code completion tasks .

These advancements in long-context code models demonstrate improvements in model performance, evaluation metrics, attention mechanisms, and code completion techniques, enhancing the capabilities and efficiency of language models in code-related tasks.


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 long-context code models. Noteworthy researchers in this area include Amanda Bertsch, Uri Alon, Graham Neubig, Matthew Gormley, Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, and many others . One key solution mentioned in the papers is the development of long-range transformers with unlimited length input, such as the Unlimiformer model . This solution aims to enhance the performance of language models on long-context code tasks by allowing them to process extensive input sequences effectively.


How were the experiments in the paper designed?

The experiments in the paper were designed as follows:

  • The dataset was created by the JetBrains Research team for the purpose of evaluating how well machine learning models can utilize data from an entire software project for code generation tasks .
  • The data collection process involved using the GitHub API to collect the initial list of repositories, followed by manual verification and assessment by the authors of the paper .
  • The dataset construction took place between October 2023 and January 2024 .
  • The dataset consists of 150 samples from 62 libraries, with each sample heavily relying on the APIs of the respective project .
  • The experiments involved assessing the quality of models in the library-based code generation task by developing and evaluating multiple baseline solutions and proposing metrics such as ChrF and API Recall for quality assessment .
  • Various language models were evaluated in two setups, including proprietary models like GPT-3.5-turbo and GPT-4, as well as open-source models like CodeLlama-7B, CodeLlama-70B, Mistral-7B, and Mixtral-8x7B .
  • The experiments aimed to measure the similarity between generated code and human-written code using metrics like ChrF and API Recall, which assess the usage of library-specific methods and classes in the generated code .
  • The dataset was used to evaluate the quality of models in tasks like library-based code generation and module summarization, introducing metrics like CompScore to assess the generated documentation quality .

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

The dataset used for quantitative evaluation in the study is the CommitChronicle dataset . The code in the dataset was collected from openly available GitHub repositories with permissive licenses, ensuring that the data found was intended to be shared freely . The dataset consists of code and artifacts written by human users on GitHub, but the focus is on the code itself rather than personal information or authorship details . The dataset is publicly available on the internet and can be accessed through a DOI at the HuggingFace Hub . The terms of use require that any research conducted using this dataset makes resulting papers available as open access, aligning with GitHub's requirements .


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 verification. The paper outlines a detailed methodology for evaluating models on repository-level long-context real-life tasks, specifically focusing on the CI builds repair benchmark to test models in fixing real-life issues in continuous integration . The dataset includes real issues describing bugs and their respective pull requests, aiming to evaluate models' abilities in locating files that need to be changed given a bug description . This structured approach ensures a rigorous evaluation of the models' performance in real-world scenarios.

Moreover, the paper describes the use of a new metric called CompScore to assess the quality of generated documentation by feeding relevant code and two versions of documentation to an assessor LLM . This metric calculates the probability that the generated documentation is superior, providing a quantitative measure to evaluate the models' performance objectively. Additionally, the experiments involve running several LLMs on a collected module summarization dataset with different lengths of relevant code context, further enhancing the robustness of the evaluation process .

Furthermore, the paper discusses the maintenance plan for the dataset, indicating that it will be extended to include more languages and samples over time . This continuous improvement and expansion of the dataset ensure that the experiments and results remain relevant and up-to-date, supporting ongoing scientific inquiry and hypothesis verification. Overall, the comprehensive methodology, detailed evaluation metrics, and planned dataset enhancements demonstrate a strong foundation for verifying scientific hypotheses in the field of code models and generation.


What are the contributions of this paper?

The paper provides several key contributions:

  • It introduces Unlimiformer, a model that extends transformers to handle unlimited length input .
  • It presents research on improving language models by retrieving from trillions of tokens .
  • The paper evaluates large language models trained on code, exploring their potential as an alternative to human evaluations .
  • It discusses the feature of commit message generation in GitHub Copilot and JetBrains IDEs .
  • The paper also delves into the evaluation of large language models for code generation, specifically focusing on the correctness of code generated by ChatPGT .

What work can be continued in depth?

The Long Code Arena project aims to stimulate research in ML-based solutions for software engineering tasks by providing a suite of benchmarks that require considering complex contexts . Future work on the Long Code Arena includes extending datasets to other programming languages, collecting data for fine-tuning models for specific tasks, and evaluating more models on the benchmarks . Researchers are encouraged to advance the field of ML-enabled software engineering by leveraging the Long Code Arena benchmarks to address tasks such as code generation, repair, completion, and summarization .

To further advance the field, researchers can focus on enhancing models' capabilities to process long-context windows efficiently, as supported context sizes have significantly increased in recent years . Additionally, exploring tasks beyond single-file contexts, such as project-wide context tasks, can help bridge the gap in benchmarks for code processing that require a broader scope . This expansion can lead to more comprehensive evaluations and advancements in ML-enabled software engineering .

Continued efforts in developing benchmarks for tasks like library-based code generation, CI builds repair, project-level code completion, commit message generation, bug localization, and module summarization can provide valuable insights into the performance of models in handling real-life software engineering challenges . By designing tasks that necessitate utilizing information from project modules or entire repositories, researchers can push the boundaries of ML4SE models and enhance their practical applicability .

Moreover, ongoing work on updating datasets with new instances, correcting labeling errors, and removing obsolete data points is crucial to maintaining the relevance and quality of the benchmarks over time . Researchers can contribute to the Long Code Arena project by extending, augmenting, or building on the existing datasets, thereby fostering collaboration and advancements in ML-enabled software engineering .

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