Introducing CoderUJB: A New Benchmark for LLMs

Large language models (LLMs) have become important tools in software engineering. They can help in many tasks, but to use them effectively, we need good ways to test their abilities. Current testing methods often miss important aspects of real-world coding. To fill this gap, we propose a new benchmark called CoderUJB. This benchmark is focused on Java programming tasks that reflect actual coding situations. By doing this, we can better evaluate how well the LLMs perform in real-life scenarios.

#The Need for Better Benchmarks

As software development grows more complicated, it is essential to have benchmarks that truly represent the challenges developers face. Many existing benchmarks focus on simple tasks and do not capture the multi-tasking nature of real coding work. This can lead to a misunderstanding of how well LLMs can handle practical tasks. Therefore, CoderUJB aims to measure LLM performance in a more comprehensive way, making it relevant for today's software development.

#What is CoderUJB?

CoderUJB is designed to test LLMs through various Java programming tasks. It is built on a collection of 2,239 code questions extracted from 17 actual open-source Java projects. These tasks cover five areas: functional code generation, code-based test generation, issue-based test generation, Defect Detection, and Automated Program Repair. Each question comes with the context it needs to be executed in a real program, allowing for more meaningful evaluation.

#How CoderUJB was Created

The creation process of CoderUJB involved several careful steps. We started with open-source Java projects known for their quality. From these projects, we were able to gather a diverse set of coding questions that reflect real-world scenarios. Each coding question was analyzed for its complexity and relevance, ensuring that the final set is robust and useful for evaluation.

#Types of Tasks in CoderUJB

#Functional Code Generation (FCG)

In functional code generation, the task is to create a function based on given function annotations. This simulates a common coding task where developers must implement functionality according to specifications.

#Code-based Test Generation (CTG)

This task requires generating test cases that check if a given piece of code works as it should. It involves understanding the logic behind the code and creating tests that confirm its correct behavior.

#Issue-based Test Generation (ITG)

Here, the LLM analyzes bug reports and generates tests designed to reproduce the issues mentioned in those reports. This task is vital for ensuring software quality and reliability.

#Defect Detection (DD)

Defect detection focuses on identifying bugs in a piece of code. LLMs need to check for potential errors such as logical mistakes that could lead to unexpected outcomes.

#Automated Program Repair (APR)

Once defects are found, the next step is to fix them. In the automated program repair task, LLMs receive faulty code and are expected to provide corrected versions.

#Factors Influencing LLM Performance

When evaluating LLMs using CoderUJB, several key factors play a role in their performance.

#The Role of Context

Providing LLMs with a complete program context has proven beneficial. It allows them to leverage all relevant details needed for the coding tasks, resulting in better performance across various programming challenges.

#Open-source vs. Closed-source LLMs

Both types of LLMs were tested using CoderUJB. Open-source models have shown great promise, sometimes matching or surpassing closed-source models in specific tasks. However, closed-source models tend to perform better in tasks requiring deeper expertise or more complex problem-solving.

#The Impact of Continued Pre-Training

When LLMs undergo additional training focused on a specific programming language, performance can improve in tasks related to that language. However, this can also lead to decreased performance in other languages. Thus, there is a balance to strike when deciding how to train these models.

#Effects of Instruction Fine-Tuning

Instruction fine-tuning involves training LLMs with varied tasks to enhance their performance. While this can be effective, results can vary widely depending on the task. In some cases, instruction fine-tuned models performed worse on tasks similar to their training, highlighting the importance of careful consideration in the training process.

#Study on LLMs Using CoderUJB

A comprehensive study was conducted to further explore the capabilities of various LLMs using CoderUJB. This study focused on several key questions:

1. Does providing program context improve LLM performance?
2. How do open-source models compare to closed-source models?
3. What is the effect of continued pre-training on performance?
4. How does instruction fine-tuning influence outcomes?

The study yielded various insights into the functioning of LLMs in practical coding tasks.

#Results and Insights

#Program Context Improves Performance

The findings suggest that providing background context significantly enhances LLM performance. For tasks like functional code generation and code-based test generation, LLMs that received detailed context produced better results compared to those that used simpler prompting methods.

#Performance Disparities Between Open-source and Closed-source LLMs

The evaluation highlighted clear differences in performance between open-source and closed-source models across different tasks. While some open-source models performed admirably, they still did not universally match the performance levels of the best closed-source models, particularly in more complex scenarios.

#Continued Pre-Training: A Double-Edged Sword

The impact of further training on specific programming languages can be mixed. Enhanced performance in related tasks was observed, but in some instances, it negatively affected performance in unrelated tasks. This indicates a need for caution when choosing training methods.

#Instruction Fine-Tuning: Variable Outcomes

Instruction fine-tuning produced varying results. It was beneficial for tasks that differed from the pre-training tasks but often hindered performance when tasks were closely aligned with the original pre-training. This inconsistency underscores the importance of understanding the context of task relevance.

#Conclusion and Future Directions

CoderUJB represents a crucial step forward in assessing the performance of LLMs in software engineering. It provides a more accurate measure of LLMs' coding capabilities in real-world scenarios. Our research indicates the significance of program context and the complexities of specialized training methods. Future research can build on these insights to refine the training processes and prompt designs for LLMs, ultimately enhancing their capabilities in diverse programming tasks.

Through ongoing exploration, we can further improve how LLMs serve as valuable tools for software engineers, helping make coding tasks more efficient and effective. As the landscape of software development evolves, the need for adaptable, high-performing models will continue to grow. This benchmark sets the stage for such advancements, paving the way for future innovations in software engineering tools.