Can AI Beat Undergraduates in Entry-level Java Assignments? Benchmarking Large Language Models on JavaBench

Jialun Cao, Zhiyong Chen, Jiarong Wu, Shing-chi Cheung, Chang Xu·June 10, 2024

Summary

The paper presents JavaBench, a project-level benchmark for evaluating large language models (LLMs) in Java code generation, focusing on OOP features. It consists of four Java projects with 389 methods, designed to test LLMs' ability to handle complex OOP concepts in three context settings and five synthesis strategies. Results show that while LLMs demonstrate progress, they lag behind undergraduate students in project-level programming, indicating a need for more comprehensive evaluations. Studies analyze model performance, context effects, and the importance of prompt engineering, highlighting the challenges and gaps in current LLM capabilities for Java development. The benchmark and findings contribute to the ongoing research on improving LLMs for code-related tasks.

Key findings

10

Paper digest

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

The paper aims to address three significant imbalances in existing code generation benchmarks and proposes a solution by introducing JavaBench, a project-level Java benchmark that focuses on Object-Oriented Programming (OOP) features . This problem of imbalanced benchmarks in terms of programming languages, code granularity, and lacking advanced OOP feature assessment is identified as a new problem in the context of evaluating Large Language Models (LLMs) .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to the capabilities of Large Language Models (LLMs) in generating Java code, specifically focusing on Object-Oriented Programming (OOP) features . The study addresses the imbalance in existing code generation benchmarks, highlighting the need to assess LLMs' ability to handle advanced OOP concepts such as encapsulation, inheritance, and polymorphism, which are crucial in real-world Java project development . The proposed JavaBench project-level benchmark is designed to evaluate LLMs on OOP features by providing a comprehensive test suite that covers Java projects with a high test coverage rate . The research introduces a systematic evaluation design that includes different context settings, synthesis strategies, and hierarchical metrics to assess LLMs' performance against JavaBench, aiming to fill the gaps in existing benchmarks and provide insights into LLMs' capabilities in generating Java code with OOP features .


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

The paper "Can AI Beat Undergraduates in Entry-level Java Assignments? Benchmarking Large Language Models on JavaBench" proposes several new ideas, methods, and models in the field of code generation and evaluation .

  1. Context Settings and Synthesis Strategies: The paper introduces three context settings for generating methods in Java classes: Entire Project, Selected Context, and Completed Project. It also outlines three synthesis strategies for completing methods in classes: Independent Synthesis, Holistic Synthesis, and Incremental Synthesis .

  2. Evaluation Design: The paper presents a systematic evaluation design to assess Large Language Models (LLMs) under different context settings and evaluation granularities using progressive metrics. It evaluates the LLMs' capabilities in handling Object-Oriented Programming (OOP) features and provides insights into their strengths and weaknesses .

  3. Benchmark Construction: The paper introduces JavaBench, a benchmark format for Java projects that includes natural language descriptions, code skeletons with multiple classes, and methods with TODOs to be completed by LLMs. It also compares JavaBench with existing benchmarks in terms of language, granularity, number of functions, classes, average test cases, and average lines of code .

  4. Generation Pipeline: The paper illustrates a generation pipeline for a Java project, detailing the process of completing methods with TODOs using different context settings and synthesis strategies. It emphasizes the importance of providing informative context while minimizing input tokens for effective code generation .

  5. Evaluation Granularity and Metrics: The paper discusses evaluation at two granularities: Class-wise and Test-wise. It evaluates whether the TODOs are completed by LLMs and considers multiple completed classes involved in a test class. It also highlights the importance of evaluating synthesized code based on different metrics and strategies .

Overall, the paper introduces innovative approaches to code generation and evaluation, focusing on enhancing LLMs' capabilities in completing Java assignments and providing a structured framework for assessing their performance in handling OOP features and generating code effectively . The paper "Can AI Beat Undergraduates in Entry-level Java Assignments? Benchmarking Large Language Models on JavaBench" introduces novel characteristics and advantages compared to previous methods in code generation and evaluation .

  1. Context Settings: The paper proposes three context settings in the synthesis pipeline: Maximum Context, Minimum Context, and Selected Context. The Maximum Context provides the entire project skeleton to LLMs, offering extensive information but potentially overwhelming the models. The Minimum Context limits the input to only the class to be completed, focusing on input token limits. The Selected Context, which includes related class/method signatures, strikes a balance between rich information and limited input tokens, enhancing synthesis effectiveness .

  2. Synthesis Strategies: The paper introduces three synthesis strategies: Independent Synthesis, Holistic Synthesis, and Incremental Synthesis. These strategies dictate how methods in classes are generated, with Incremental Synthesis considering different orders like sequential, reverse, and random. The Incremental Synthesis strategy provides insights into the impact of method generation order on LLM performance, offering a structured approach to code completion .

  3. Evaluation Granularity and Metrics: The paper evaluates synthesized projects at two granularities: Class-wise and Test-wise. It assesses whether the TODOs are completed by LLMs and evaluates multiple completed classes involved in a test class. By considering different evaluation granularities and metrics, the paper provides a comprehensive analysis of LLM performance in completing Java assignments .

  4. Advantages Over Previous Methods: The paper's approach offers several advantages over previous methods. By incorporating Selected Context, the paper achieves a 68.61% Pass@1 rate, demonstrating the effectiveness of this context setting in code completion tasks. The study also shows that increasing the number of trials (𝑘) from 1 to 5 leads to further improvements in completion, compilation, and pass rates, highlighting the impact of multiple trials on LLM performance. Additionally, the paper's holistic synthesis strategy yields an average test-wise Pass@5 of 41.17%, indicating progress in LLM capabilities for Java project-level programming .

Overall, the paper's innovative context settings, synthesis strategies, and evaluation design contribute to enhancing LLM performance in completing Java assignments, offering a structured framework for benchmarking and assessing LLM capabilities in code generation 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 benchmarking large language models for code generation. Noteworthy researchers in this field include Jialun Cao, Zhiyong Chen, Shing-Chi Cheung, and Chang Xu . The key solution mentioned in the paper "Can AI Beat Undergraduates in Entry-level Java Assignments? Benchmarking Large Language Models on JavaBench" is the proposal of JavaBench, a project-level Java benchmark that focuses on exercising Object-Oriented Programming (OOP) features. JavaBench comprises four Java projects with 389 methods in 106 Java classes, ensuring a high test coverage of up to 92% .


How were the experiments in the paper designed?

The experiments in the paper were designed with a specific methodology:

  • The experiments involved assessing the performance of Large Language Models (LLMs) in completing Java programming assignments .
  • The experiments considered different synthesis strategies for generating code, including Independent Synthesis, Holistic Synthesis, and Incremental Synthesis .
  • The evaluation of the experiments was conducted at two granularities: Class-wise evaluation and Test-wise evaluation .
  • The evaluation metrics used in the experiments included assessing whether the LLMs completed the assigned tasks and evaluating the completed classes involved in test cases .
  • The experiments aimed to explore the capabilities of LLMs in completing Java programming tasks and to evaluate the effectiveness of different synthesis strategies in generating code .

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

The dataset used for quantitative evaluation in the study is JavaBench . The code used in the evaluation is open source as it is built from entry-level projects designed to assess students' coding ability .


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 JavaBench, a project-level Java benchmark focusing on Object-Oriented Programming (OOP) features, which addresses the imbalances in existing benchmarks related to programming languages, code granularity, and advanced coding skills . The JavaBench consists of four Java projects with 389 methods in 106 Java classes, attested by 282 undergraduate students with an average pass rate of 90.93/100 against the test suite, demonstrating the quality of documentation, code skeleton, and tests .

Furthermore, the study outlines a systematic evaluation design that covers three context settings and five synthesis strategies at two granularities using three hierarchical metrics, providing a comprehensive framework for evaluating LLM's capabilities against JavaBench . The experiments conducted in the study yield insightful findings, highlighting the effectiveness of the proposed JavaBench in evaluating LLMs on handling OOP features and project-level Java tasks .

Overall, the experiments and results in the paper offer robust empirical evidence to support the scientific hypotheses put forth, demonstrating the effectiveness of JavaBench in evaluating LLMs' performance in Java programming tasks, particularly focusing on OOP features and project-level challenges .


What are the contributions of this paper?

The paper "Can AI Beat Undergraduates in Entry-level Java Assignments? Benchmarking Large Language Models on JavaBench" makes several key contributions:

  • Introduction of JavaBench: The paper introduces JavaBench, a project-level Java benchmark that focuses on Object-Oriented Programming (OOP) features, addressing the imbalance in existing benchmarks that primarily assess basic coding skills .
  • Evaluation Design: It proposes a systematic evaluation design covering three context settings and five synthesis strategies at two granularities using three hierarchical metrics, providing a comprehensive evaluation framework for assessing the performance of Large Language Models (LLMs) against JavaBench .
  • Research Questions and Findings: The paper addresses several research questions, including context selection, incremental strategies, and bad case analysis, leading to insights such as the impact of context settings on LLM performance and the importance of synthesis strategies in generating Java code .
  • Performance Analysis: It presents an analysis of the overall performance of various LLMs on JavaBench, highlighting metrics such as Completion@1, Compilation@1, and Pass@1, and identifies the best-performing models based on different synthesis strategies .
  • Visualization and Comparison: The paper visualizes the number of characters used in different context settings, compares the effectiveness of various synthesis strategies, and provides insights into the importance of context and strategy selection in code generation tasks .

What work can be continued in depth?

Further work that can be continued in depth includes exploring the impact of synthesis strategies on the performance of Large Language Models (LLMs) in completing Java projects. The study considers three synthesis strategies: Independent Synthesis, Holistic Synthesis, and Incremental Synthesis, with variations in the order of synthesizing methods within each class . This exploration can provide insights into how different synthesis strategies affect the ability of LLMs to generate code accurately and efficiently in Java projects.

Tables

3

Introduction
Background
Emergence of large language models in code generation
Importance of OOP features in software development
Objective
To assess LLM performance in Java OOP tasks
Identify gaps and challenges for project-level programming
Methodology
Data Collection
Project Selection
Four diverse Java projects with 389 methods
Focus on OOP concepts and context settings
Benchmarked Models
Selection of prominent LLMs for comparison
Data Synthesis Strategies
Five strategies to test LLMs' code generation abilities
Context variations: three different scenarios
Evaluation Metrics
Comparison with undergraduate student performance
Quantitative analysis of model accuracy and efficiency
Results and Analysis
Model Performance
Overall benchmark results for LLMs
Strengths and weaknesses in OOP feature handling
Context Effects
Impact of different context settings on model performance
Lessons learned from context-dependent code generation
Prompt Engineering
The role of prompt design in LLM output quality
Challenges and best practices for effective prompts
Gap Analysis
Where LLMs lag behind human programmers, particularly undergraduates
Implications for future model improvements
Discussion
Limitations and future directions of the study
Importance of comprehensive evaluations for code-related tasks
Conclusion
Summary of findings and contributions to the field
Recommendations for LLM developers and researchers
Future Work
Directions for improving LLMs in Java OOP code generation
Potential collaboration opportunities with the community
Basic info
papers
software engineering
machine learning
programming languages
artificial intelligence
Advanced features
Insights
What is JavaBench used for in evaluating LLMs?
How many methods are included in the four Java projects in JavaBench?
What are the three context settings in which the benchmark tests LLMs' OOP capabilities?
How do the results of the study compare LLMs to undergraduate students in project-level programming?

Can AI Beat Undergraduates in Entry-level Java Assignments? Benchmarking Large Language Models on JavaBench

Jialun Cao, Zhiyong Chen, Jiarong Wu, Shing-chi Cheung, Chang Xu·June 10, 2024

Summary

The paper presents JavaBench, a project-level benchmark for evaluating large language models (LLMs) in Java code generation, focusing on OOP features. It consists of four Java projects with 389 methods, designed to test LLMs' ability to handle complex OOP concepts in three context settings and five synthesis strategies. Results show that while LLMs demonstrate progress, they lag behind undergraduate students in project-level programming, indicating a need for more comprehensive evaluations. Studies analyze model performance, context effects, and the importance of prompt engineering, highlighting the challenges and gaps in current LLM capabilities for Java development. The benchmark and findings contribute to the ongoing research on improving LLMs for code-related tasks.
Mind map
Selection of prominent LLMs for comparison
Focus on OOP concepts and context settings
Four diverse Java projects with 389 methods
Implications for future model improvements
Where LLMs lag behind human programmers, particularly undergraduates
Challenges and best practices for effective prompts
The role of prompt design in LLM output quality
Lessons learned from context-dependent code generation
Impact of different context settings on model performance
Strengths and weaknesses in OOP feature handling
Overall benchmark results for LLMs
Quantitative analysis of model accuracy and efficiency
Comparison with undergraduate student performance
Context variations: three different scenarios
Five strategies to test LLMs' code generation abilities
Benchmarked Models
Project Selection
Identify gaps and challenges for project-level programming
To assess LLM performance in Java OOP tasks
Importance of OOP features in software development
Emergence of large language models in code generation
Potential collaboration opportunities with the community
Directions for improving LLMs in Java OOP code generation
Recommendations for LLM developers and researchers
Summary of findings and contributions to the field
Importance of comprehensive evaluations for code-related tasks
Limitations and future directions of the study
Gap Analysis
Prompt Engineering
Context Effects
Model Performance
Evaluation Metrics
Data Synthesis Strategies
Data Collection
Objective
Background
Future Work
Conclusion
Discussion
Results and Analysis
Methodology
Introduction
Outline
Introduction
Background
Emergence of large language models in code generation
Importance of OOP features in software development
Objective
To assess LLM performance in Java OOP tasks
Identify gaps and challenges for project-level programming
Methodology
Data Collection
Project Selection
Four diverse Java projects with 389 methods
Focus on OOP concepts and context settings
Benchmarked Models
Selection of prominent LLMs for comparison
Data Synthesis Strategies
Five strategies to test LLMs' code generation abilities
Context variations: three different scenarios
Evaluation Metrics
Comparison with undergraduate student performance
Quantitative analysis of model accuracy and efficiency
Results and Analysis
Model Performance
Overall benchmark results for LLMs
Strengths and weaknesses in OOP feature handling
Context Effects
Impact of different context settings on model performance
Lessons learned from context-dependent code generation
Prompt Engineering
The role of prompt design in LLM output quality
Challenges and best practices for effective prompts
Gap Analysis
Where LLMs lag behind human programmers, particularly undergraduates
Implications for future model improvements
Discussion
Limitations and future directions of the study
Importance of comprehensive evaluations for code-related tasks
Conclusion
Summary of findings and contributions to the field
Recommendations for LLM developers and researchers
Future Work
Directions for improving LLMs in Java OOP code generation
Potential collaboration opportunities with the community
Key findings
10

Paper digest

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

The paper aims to address three significant imbalances in existing code generation benchmarks and proposes a solution by introducing JavaBench, a project-level Java benchmark that focuses on Object-Oriented Programming (OOP) features . This problem of imbalanced benchmarks in terms of programming languages, code granularity, and lacking advanced OOP feature assessment is identified as a new problem in the context of evaluating Large Language Models (LLMs) .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to the capabilities of Large Language Models (LLMs) in generating Java code, specifically focusing on Object-Oriented Programming (OOP) features . The study addresses the imbalance in existing code generation benchmarks, highlighting the need to assess LLMs' ability to handle advanced OOP concepts such as encapsulation, inheritance, and polymorphism, which are crucial in real-world Java project development . The proposed JavaBench project-level benchmark is designed to evaluate LLMs on OOP features by providing a comprehensive test suite that covers Java projects with a high test coverage rate . The research introduces a systematic evaluation design that includes different context settings, synthesis strategies, and hierarchical metrics to assess LLMs' performance against JavaBench, aiming to fill the gaps in existing benchmarks and provide insights into LLMs' capabilities in generating Java code with OOP features .


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

The paper "Can AI Beat Undergraduates in Entry-level Java Assignments? Benchmarking Large Language Models on JavaBench" proposes several new ideas, methods, and models in the field of code generation and evaluation .

  1. Context Settings and Synthesis Strategies: The paper introduces three context settings for generating methods in Java classes: Entire Project, Selected Context, and Completed Project. It also outlines three synthesis strategies for completing methods in classes: Independent Synthesis, Holistic Synthesis, and Incremental Synthesis .

  2. Evaluation Design: The paper presents a systematic evaluation design to assess Large Language Models (LLMs) under different context settings and evaluation granularities using progressive metrics. It evaluates the LLMs' capabilities in handling Object-Oriented Programming (OOP) features and provides insights into their strengths and weaknesses .

  3. Benchmark Construction: The paper introduces JavaBench, a benchmark format for Java projects that includes natural language descriptions, code skeletons with multiple classes, and methods with TODOs to be completed by LLMs. It also compares JavaBench with existing benchmarks in terms of language, granularity, number of functions, classes, average test cases, and average lines of code .

  4. Generation Pipeline: The paper illustrates a generation pipeline for a Java project, detailing the process of completing methods with TODOs using different context settings and synthesis strategies. It emphasizes the importance of providing informative context while minimizing input tokens for effective code generation .

  5. Evaluation Granularity and Metrics: The paper discusses evaluation at two granularities: Class-wise and Test-wise. It evaluates whether the TODOs are completed by LLMs and considers multiple completed classes involved in a test class. It also highlights the importance of evaluating synthesized code based on different metrics and strategies .

Overall, the paper introduces innovative approaches to code generation and evaluation, focusing on enhancing LLMs' capabilities in completing Java assignments and providing a structured framework for assessing their performance in handling OOP features and generating code effectively . The paper "Can AI Beat Undergraduates in Entry-level Java Assignments? Benchmarking Large Language Models on JavaBench" introduces novel characteristics and advantages compared to previous methods in code generation and evaluation .

  1. Context Settings: The paper proposes three context settings in the synthesis pipeline: Maximum Context, Minimum Context, and Selected Context. The Maximum Context provides the entire project skeleton to LLMs, offering extensive information but potentially overwhelming the models. The Minimum Context limits the input to only the class to be completed, focusing on input token limits. The Selected Context, which includes related class/method signatures, strikes a balance between rich information and limited input tokens, enhancing synthesis effectiveness .

  2. Synthesis Strategies: The paper introduces three synthesis strategies: Independent Synthesis, Holistic Synthesis, and Incremental Synthesis. These strategies dictate how methods in classes are generated, with Incremental Synthesis considering different orders like sequential, reverse, and random. The Incremental Synthesis strategy provides insights into the impact of method generation order on LLM performance, offering a structured approach to code completion .

  3. Evaluation Granularity and Metrics: The paper evaluates synthesized projects at two granularities: Class-wise and Test-wise. It assesses whether the TODOs are completed by LLMs and evaluates multiple completed classes involved in a test class. By considering different evaluation granularities and metrics, the paper provides a comprehensive analysis of LLM performance in completing Java assignments .

  4. Advantages Over Previous Methods: The paper's approach offers several advantages over previous methods. By incorporating Selected Context, the paper achieves a 68.61% Pass@1 rate, demonstrating the effectiveness of this context setting in code completion tasks. The study also shows that increasing the number of trials (𝑘) from 1 to 5 leads to further improvements in completion, compilation, and pass rates, highlighting the impact of multiple trials on LLM performance. Additionally, the paper's holistic synthesis strategy yields an average test-wise Pass@5 of 41.17%, indicating progress in LLM capabilities for Java project-level programming .

Overall, the paper's innovative context settings, synthesis strategies, and evaluation design contribute to enhancing LLM performance in completing Java assignments, offering a structured framework for benchmarking and assessing LLM capabilities in code generation 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 benchmarking large language models for code generation. Noteworthy researchers in this field include Jialun Cao, Zhiyong Chen, Shing-Chi Cheung, and Chang Xu . The key solution mentioned in the paper "Can AI Beat Undergraduates in Entry-level Java Assignments? Benchmarking Large Language Models on JavaBench" is the proposal of JavaBench, a project-level Java benchmark that focuses on exercising Object-Oriented Programming (OOP) features. JavaBench comprises four Java projects with 389 methods in 106 Java classes, ensuring a high test coverage of up to 92% .


How were the experiments in the paper designed?

The experiments in the paper were designed with a specific methodology:

  • The experiments involved assessing the performance of Large Language Models (LLMs) in completing Java programming assignments .
  • The experiments considered different synthesis strategies for generating code, including Independent Synthesis, Holistic Synthesis, and Incremental Synthesis .
  • The evaluation of the experiments was conducted at two granularities: Class-wise evaluation and Test-wise evaluation .
  • The evaluation metrics used in the experiments included assessing whether the LLMs completed the assigned tasks and evaluating the completed classes involved in test cases .
  • The experiments aimed to explore the capabilities of LLMs in completing Java programming tasks and to evaluate the effectiveness of different synthesis strategies in generating code .

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

The dataset used for quantitative evaluation in the study is JavaBench . The code used in the evaluation is open source as it is built from entry-level projects designed to assess students' coding ability .


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 JavaBench, a project-level Java benchmark focusing on Object-Oriented Programming (OOP) features, which addresses the imbalances in existing benchmarks related to programming languages, code granularity, and advanced coding skills . The JavaBench consists of four Java projects with 389 methods in 106 Java classes, attested by 282 undergraduate students with an average pass rate of 90.93/100 against the test suite, demonstrating the quality of documentation, code skeleton, and tests .

Furthermore, the study outlines a systematic evaluation design that covers three context settings and five synthesis strategies at two granularities using three hierarchical metrics, providing a comprehensive framework for evaluating LLM's capabilities against JavaBench . The experiments conducted in the study yield insightful findings, highlighting the effectiveness of the proposed JavaBench in evaluating LLMs on handling OOP features and project-level Java tasks .

Overall, the experiments and results in the paper offer robust empirical evidence to support the scientific hypotheses put forth, demonstrating the effectiveness of JavaBench in evaluating LLMs' performance in Java programming tasks, particularly focusing on OOP features and project-level challenges .


What are the contributions of this paper?

The paper "Can AI Beat Undergraduates in Entry-level Java Assignments? Benchmarking Large Language Models on JavaBench" makes several key contributions:

  • Introduction of JavaBench: The paper introduces JavaBench, a project-level Java benchmark that focuses on Object-Oriented Programming (OOP) features, addressing the imbalance in existing benchmarks that primarily assess basic coding skills .
  • Evaluation Design: It proposes a systematic evaluation design covering three context settings and five synthesis strategies at two granularities using three hierarchical metrics, providing a comprehensive evaluation framework for assessing the performance of Large Language Models (LLMs) against JavaBench .
  • Research Questions and Findings: The paper addresses several research questions, including context selection, incremental strategies, and bad case analysis, leading to insights such as the impact of context settings on LLM performance and the importance of synthesis strategies in generating Java code .
  • Performance Analysis: It presents an analysis of the overall performance of various LLMs on JavaBench, highlighting metrics such as Completion@1, Compilation@1, and Pass@1, and identifies the best-performing models based on different synthesis strategies .
  • Visualization and Comparison: The paper visualizes the number of characters used in different context settings, compares the effectiveness of various synthesis strategies, and provides insights into the importance of context and strategy selection in code generation tasks .

What work can be continued in depth?

Further work that can be continued in depth includes exploring the impact of synthesis strategies on the performance of Large Language Models (LLMs) in completing Java projects. The study considers three synthesis strategies: Independent Synthesis, Holistic Synthesis, and Incremental Synthesis, with variations in the order of synthesizing methods within each class . This exploration can provide insights into how different synthesis strategies affect the ability of LLMs to generate code accurately and efficiently in Java projects.

Tables
3
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