Combining Language-Specific and Language-Generic Program Reduction Techniques

Large Language Models (LLMs) are a type of deep learning technique that can process both natural languages and programming languages with remarkable capabilities. Recent research has applied and assessed LLMs on various software engineering tasks, such as automatic program repair and program generation. While LLMs have shown promising results, they also have limitations, including difficulty in distinguishing nuances between programs and deteriorating memorizing and processing capacity as input size grows. Additionally, LLMs cannot automatically complete complex tasks without guidance. Therefore, directly asking LLMs to reduce programs with tens of thousands of lines is impractical.
To understand the potential of LLMs in software engineering, it’s essential to demystify their capabilities and limitations. Imagine a powerful AI model that can process language like a human but much faster and more accurately. With enough training data, an LLM can learn to perform various tasks, such as translating languages or generating text summaries. However, like any tool, LLMs have their limitations. They may struggle with complex instructions or require guidance to complete tasks effectively.
In software engineering, researchers have applied LLMs to automatic program repair and generation. For example, Xia et al. [40] thoroughly evaluated nine state-of-the-art LLMs across multiple datasets and programming languages, demonstrating that directly applying LLMs can significantly outperform existing APR techniques. Huang et al. [12] also performed an empirical study on the improvement brought by model fine-tuning in APR. The results showed that fine-tuning an LLM can significantly improve its performance in repairing programs.
However, despite their potential, LLMs face challenges in software engineering. One of the main limitations is their difficulty in distinguishing nuances between programs. As a result, LLMs may struggle to identify and fix errors accurately. Another challenge is the memorizing and processing capacity of LLMs, which can deteriorate as input size grows, making it impractical to use them for tasks with large inputs.
To overcome these challenges, researchers have proposed various techniques. For example, some studies have focused on developing new training methods to improve the ability of LLMs to distinguish nuances between programs. Others have explored using multi-task learning to improve the overall performance of LLMs in software engineering tasks.
In conclusion, Large Language Models hold great promise for software engineering tasks, but their limitations must be acknowledged and addressed. By understanding the capabilities and limitations of LLMs, researchers can develop more effective techniques for applying these models to various software engineering tasks, paving the way for a brighter future in this field.