Comprehensive Survey on Language Models for Code Processing: Unifying NLP and Software Engineering Perspectives

Core Concepts

This work provides a comprehensive survey of recent advancements in code processing with language models, covering a wide range of models, tasks, datasets, and related works. It highlights the historical transition from statistical and RNN-based models to pretrained Transformers and large language models, drawing parallels to the progress in natural language processing. The survey also discusses the integration of code-specific features and techniques adapted from NLP, as well as the latest applications of language models in software development.

Abstract

The authors undertake a panoramic survey of language models for code, covering over 50 models, 30 downstream tasks, 170 datasets, and 800 related works. They break down the different categories of code language models, ranging from large general-domain models to specialized smaller models, and emphasize the relations and differences between such models. The survey first contextualizes the downstream tasks in code processing, highlighting the historical transition from various code understanding tasks to more practical text-to-code generation tasks. It then provides the preliminaries of language modeling and Transformer models, before discussing the plethora of large language models (LLMs) that have demonstrated coding ability. The authors then review the specialized and often smaller models, with special attention on the recent application of infilling objectives, instruction tuning, reinforcement learning, and engineering improvements. They also discuss unique features of code, such as abstract syntax trees and control flow graphs, that have been utilized to aid code processing. Finally, the survey reviews the most recent integration between LLMs and software development, before concluding and highlighting the current challenges in code processing.

Stats

"Language modeling has advanced remarkably in recent years with the advent of pretrained Transformers (Vaswani et al., 2017) such as BERT (Devlin et al., 2019) and GPT (Radford et al., 2018)." "As large language models (LLMs) scaled to hundreds of billions of parameters and started to display early signs of artificial general intelligence (Brown et al., 2020; Chowdhery et al., 2023; OpenAI, 2023), their applications have also transcended text processing." "Pioneered by Codex (Chen et al., 2021b), LLMs have achieved impressive results in code processing, giving rise to commercial products such as GitHub Copilot and open-source multi-billion code models such as StarCoder (Li et al., 2023h) and Code LLaMA (Rozière et al., 2023)."

Quotes

"Language modeling has advanced remarkably in recent years with the advent of pretrained Transformers (Vaswani et al., 2017) such as BERT (Devlin et al., 2019) and GPT (Radford et al., 2018)." "As large language models (LLMs) scaled to hundreds of billions of parameters and started to display early signs of artificial general intelligence (Brown et al., 2020; Chowdhery et al., 2023; OpenAI, 2023), their applications have also transcended text processing." "Pioneered by Codex (Chen et al., 2021b), LLMs have achieved impressive results in code processing, giving rise to commercial products such as GitHub Copilot and open-source multi-billion code models such as StarCoder (Li et al., 2023h) and Code LLaMA (Rozière et al., 2023)."

Key Insights Distilled From

Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code

by Ziyin Zhang,... at arxiv.org 04-17-2024

https://arxiv.org/pdf/2311.07989.pdf

Deeper Inquiries

How can the integration between NLP and software engineering be further strengthened to drive the development of more capable and versatile code language models?

The integration between NLP and software engineering can be enhanced through collaborative research efforts, interdisciplinary workshops, and shared resources. One approach is to establish joint research projects that bring together experts from both fields to work on code language model development. This collaboration can lead to the exchange of knowledge, methodologies, and best practices, ultimately improving the quality and capabilities of code language models. Furthermore, organizing interdisciplinary workshops and conferences that focus on the intersection of NLP and software engineering can facilitate discussions, idea sharing, and networking among researchers and practitioners in both domains. These events can serve as platforms for presenting new research findings, discussing challenges, and identifying opportunities for collaboration. Shared resources, such as datasets, evaluation benchmarks, and code repositories, can also play a crucial role in strengthening the integration between NLP and software engineering. By making these resources openly available to researchers from both fields, it becomes easier to benchmark and compare different approaches, leading to advancements in code language model development. Overall, fostering a strong and collaborative relationship between the NLP and software engineering communities can drive the development of more capable and versatile code language models by leveraging the strengths and expertise of both domains.

What are the potential limitations and risks of using large language models for critical software engineering tasks, and how can they be mitigated?

While large language models (LLMs) have shown great promise in various tasks, including code processing, there are potential limitations and risks associated with their use in critical software engineering tasks. Some of these limitations and risks include: Bias and Fairness: LLMs trained on biased data can perpetuate and amplify biases in software engineering tasks, leading to unfair outcomes. Mitigation strategies include diverse and representative training data, bias detection mechanisms, and fairness-aware model evaluation. Interpretability: LLMs are often considered black boxes, making it challenging to interpret their decisions and reasoning processes. Techniques such as attention visualization, model distillation, and post-hoc explanation methods can help improve model interpretability. Data Privacy and Security: LLMs trained on sensitive code repositories may inadvertently leak confidential information or introduce security vulnerabilities. Secure data handling practices, encryption techniques, and privacy-preserving training methods can mitigate these risks. Resource Intensiveness: Training and deploying large language models require significant computational resources, which may not be feasible for all software engineering teams. Model compression, knowledge distillation, and efficient model architectures can help reduce resource requirements. Robustness and Generalization: LLMs may struggle with generalizing to unseen code patterns or handling edge cases, leading to performance degradation in critical tasks. Robust training strategies, data augmentation techniques, and adversarial testing can enhance model generalization and robustness. To mitigate these limitations and risks, a holistic approach that combines technical solutions, ethical considerations, and best practices in model development and deployment is essential. Collaboration between researchers, practitioners, and policymakers can help address these challenges and ensure the responsible use of LLMs in critical software engineering tasks.

Given the unique characteristics of code, what novel architectural designs or training objectives could be explored to create more efficient and effective code language models?

To enhance the efficiency and effectiveness of code language models, exploring novel architectural designs and training objectives tailored to the unique characteristics of code is crucial. Some potential avenues for innovation include: Graph Neural Networks (GNNs): Leveraging GNNs to capture the structural dependencies and relationships within code, such as control flow graphs and data flow graphs. GNN-based architectures can effectively model the hierarchical nature of code and improve code understanding and generation tasks. Hybrid Models: Developing hybrid models that combine the strengths of Transformers for capturing long-range dependencies in code with the interpretability of graph-based models for understanding code structures. This hybrid approach can lead to more accurate and interpretable code language models. Multi-Task Learning: Training code language models on multiple related tasks simultaneously, such as code summarization, defect detection, and code completion. Multi-task learning can help the model learn diverse aspects of code representation and improve its overall performance across different software engineering tasks. Domain-Specific Pretraining Objectives: Designing pretraining objectives that are specific to software engineering tasks, such as code refactoring, code review analysis, or API recommendation. By incorporating domain-specific knowledge during pretraining, code language models can better capture the nuances and intricacies of software development. Attention Mechanism Variants: Exploring novel attention mechanisms, such as sparse attention, adaptive attention, or structured attention, to improve the model's focus on relevant code snippets and enhance its ability to handle long and complex code sequences efficiently. By exploring these innovative architectural designs and training objectives, researchers can push the boundaries of code language model development, leading to more efficient, effective, and specialized models tailored to the unique characteristics of code in software engineering tasks.