Evaluating the Effectiveness of Large Language Models for Generating Documentation for Legacy Code in MUMPS and Assembly Language

核心概念

While large language models (LLMs) show promise for generating useful documentation for legacy code in languages like MUMPS and Assembly Language, current automated metrics struggle to accurately assess the quality of this documentation, highlighting the need for better evaluation methods.

摘要

• Bibliographic Information: Diggs, C., Doyle, M., Madan, A., Scott, S., Escamilla, E., Zimmer, J., ... & Thaker, S. (2024). Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation. arXiv preprint arXiv:2411.14971.
• Research Objective: This research investigates the use of LLMs to generate documentation for legacy code written in MUMPS and IBM mainframe Assembly Language Code (ALC) and explores the effectiveness of automated metrics in predicting the quality of LLM-generated documentation.
• Methodology: The researchers used four LLMs (Claude 3.0 Sonnet, Llama 3, Mixtral, and GPT-4) to generate line-wise code comments for two datasets: an electronic health records system in MUMPS and open-source applications in ALC. They developed a novel prompting strategy to prevent code modification by the LLMs and evaluated the generated comments using a rubric focused on completeness, readability, usefulness, and hallucination. Additionally, they assessed the correlation between human evaluations and automated metrics, including code complexity metrics, LLM runtime metrics, and reference-based metrics.
• Key Findings: The study found that LLM-generated comments for both MUMPS and ALC were generally hallucination-free, complete, readable, and useful compared to ground-truth comments, although ALC posed greater challenges. However, none of the automated metrics tested showed a strong correlation with human-evaluated comment quality.
• Main Conclusions: The research concludes that while LLMs demonstrate potential for generating documentation for legacy code, current automated evaluation methods are insufficient for accurately assessing the quality of this documentation. This highlights the need for more robust and reliable evaluation metrics specifically designed for LLM-generated documentation in legacy systems.
• Significance: This research addresses a critical gap in the field of legacy code modernization by exploring the potential of LLMs for documentation generation and highlighting the limitations of existing evaluation methods.
• Limitations and Future Research: The study acknowledges the limited size of the datasets and the subjective nature of human evaluation as limitations. Future research should focus on developing better evaluation metrics for LLM-generated documentation, potentially incorporating expert knowledge and domain-specific considerations. Additionally, exploring the application of LLMs to other legacy languages and larger codebases would be valuable.

统计

Eight of the ten critical federal IT legacy systems most in need of modernization lacked documented plans or had incomplete plans.
Agencies spend almost 80% of their IT budgets on operations and maintenance of legacy systems, compared to around half in the private sector.
The WorldVistA MUMPS dataset contains 5,107 lines of code and 235 developer comments across 78 files.
The zFAM ALC dataset contains 13,344 lines of code and 7,097 developer comments across 12 files.
Inter-rater reliability among SMEs for MUMPS comments was moderate to good, with ICC values ranging from 0.65 to 0.89.
Inter-rater reliability for ALC comments was poor, with ICC values ranging from 0.12 to 0.22.
GPT-4 Turbo achieved an average rating of 9.1 out of 10 for factualness on MUMPS code.
Llama3 approached the factualness level of ground-truth comments on ALC (6.53/10 vs. 7.31/10).

引用

"Legacy systems are systems that contain outdated computer software. These systems are often written in antiquated languages like COBOL, MUMPS, or historical dialects of mainframe assembly which make maintenance and further development challenging."
"While documentation generation methods have advanced greatly in recent years [15]–[17], they have been trained and evaluated primarily on mainstream languages like C, Python, and Java, and on relatively short, simple programs with limited complexity [18]."
"In the emerging landscape of LLM-based code understanding tasks, documentation generation, or code summarization, emerges as a compelling modernization strategy."

从中提取的关键见解

Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation

by Colin Diggs,... 在 arxiv.org 11-25-2024

https://arxiv.org/pdf/2411.14971.pdf

更深入的查询

How might the increasing availability of open-source legacy code impact the development and evaluation of LLM-based documentation generation tools?

The increasing availability of open-source legacy code presents a significant opportunity for advancing LLM-based documentation generation tools in several ways:

Abundant Training Data: Open-source legacy code provides a wealth of data to train LLMs specifically on the nuances of legacy languages like MUMPS and ALC. This can lead to the development of specialized models with a deeper understanding of these languages, improving the accuracy and relevance of generated documentation.
Benchmark Datasets: The availability of diverse open-source legacy codebases enables the creation of comprehensive benchmark datasets for evaluating LLM performance on documentation generation tasks. This allows for standardized evaluation and comparison of different models and approaches, driving further innovation in the field.
Real-World Complexity: Open-source legacy code often reflects the complexities and challenges of real-world systems, including technical debt and obscure coding practices. Training and evaluating LLMs on such codebases ensures that the resulting tools are robust and applicable to practical modernization scenarios.
Community-Driven Development: The open-source nature fosters collaboration and knowledge sharing among researchers and developers working on LLM-based documentation generation. This can accelerate the development of new techniques and tools, as well as the identification and mitigation of limitations.
However, challenges remain:

Data Quality and Consistency: Open-source legacy code can vary significantly in quality and consistency, potentially impacting the effectiveness of training data. Careful curation and preprocessing of datasets are crucial to ensure the reliability of trained models.
Domain Specificity: Legacy code often exhibits domain-specific characteristics and terminology. LLMs trained on general-purpose code may struggle to generate accurate and meaningful documentation for highly specialized legacy systems.
Evaluation Metrics: As highlighted in the paper, evaluating the quality of LLM-generated documentation remains a challenge. The development of robust and standardized evaluation metrics that align with human judgment is essential for assessing the true impact of open-source legacy code on tool development.

Could the focus on generating human-readable documentation potentially limit the usefulness of LLM-generated documentation for other modernization tasks, such as automated code translation or refactoring?

Yes, focusing solely on human-readable documentation could limit the usefulness of LLM-generated documentation for other modernization tasks like automated code translation or refactoring. Here's why:

Semantic Understanding vs. Structural Representation: Human-readable documentation often focuses on explaining the "what" and "why" of code functionality, using natural language. However, automated tasks like translation or refactoring require a deeper understanding of the code's underlying structure, logic, and dependencies.
Ambiguity and Interpretation: Natural language is inherently ambiguous, and even well-written human-readable documentation can be open to interpretation. Automated tools thrive on precise and unambiguous representations of code, which might not be fully captured in human-oriented explanations.
Loss of Contextual Information: While striving for readability, some contextual details crucial for automated tasks might be omitted. For instance, information about data flow, variable scope, or specific API interactions might be abstracted away in a human-friendly explanation but are essential for accurate code transformation.
To enhance the utility of LLM-generated documentation for broader modernization tasks, consider these approaches:

Multi-Modal Documentation: Generate documentation that combines human-readable explanations with machine-interpretable representations, such as abstract syntax trees (ASTs), control flow graphs (CFGs), or data flow diagrams.
Formal Language Integration: Incorporate elements of formal specification languages or domain-specific ontologies into the documentation. This provides a more structured and unambiguous representation of code functionality and relationships.
Task-Specific Documentation: Train LLMs to generate documentation tailored to the specific needs of downstream modernization tasks. For example, documentation for code translation might prioritize semantic equivalence, while documentation for refactoring might emphasize dependencies and potential side effects.

If code is a form of language, what are the broader implications of using AI to translate between "dead" or legacy languages and their modern counterparts in other fields, such as linguistics or cultural preservation?

The concept of using AI to "translate" between legacy code and modern languages has intriguing parallels in other fields, particularly linguistics and cultural preservation, where preserving and understanding "dead" or endangered languages is crucial.
Here are some broader implications:

Revitalizing Lost Knowledge: Just as legacy code encapsulates valuable business logic and functionality, endangered languages hold unique cultural insights, historical narratives, and traditional knowledge. AI-powered translation could unlock this knowledge, making it accessible to wider audiences and future generations.
Preservation and Documentation: AI can aid in documenting endangered languages by transcribing and translating existing recordings, texts, or even oral histories. This digital preservation ensures their survival and facilitates further linguistic analysis and research.
Language Learning and Education: AI-powered tools can create interactive language learning platforms, making it easier for individuals to learn and engage with endangered languages. This can contribute to language revitalization efforts and foster cultural exchange.
Bridging Communication Gaps: In regions where endangered languages are still spoken, AI translation tools can bridge communication gaps between generations or communities, facilitating the transmission of cultural heritage and knowledge.
However, ethical considerations are paramount:

Accuracy and Bias: AI models are only as good as their training data. Biases present in the data can perpetuate stereotypes or misrepresent cultural nuances. Ensuring accuracy and mitigating bias in AI-powered translation is crucial.
Cultural Sensitivity: Language is deeply intertwined with culture and identity. AI translation should be approached with sensitivity, respecting the cultural context and avoiding the erasure or homogenization of linguistic diversity.
Community Engagement: The development and deployment of AI tools for endangered languages should involve close collaboration with communities where these languages originated. Their perspectives and expertise are essential to ensure responsible and culturally appropriate use of technology.
In conclusion, while challenges exist, the use of AI to "translate" between legacy code and modern languages has the potential to revolutionize not just software development but also fields like linguistics and cultural preservation. By carefully addressing ethical considerations and prioritizing community engagement, we can harness the power of AI to preserve cultural heritage, revitalize lost knowledge, and foster greater understanding across linguistic and technological divides.