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Exploring Large Language Models in Software Engineering: A Comprehensive Review


Core Concepts
The authors conducted a systematic literature review to understand the impact of Large Language Models (LLMs) on Software Engineering. They focused on categorizing LLMs, analyzing data collection methods, and evaluating performance in SE tasks.
Abstract
The study delves into the application of LLMs in Software Engineering, highlighting the importance of data collection, preprocessing, and model selection. It explores various types of datasets used and their impact on LLM performance in SE tasks. Large Language Models (LLMs) have revolutionized Software Engineering by optimizing processes and outcomes. The study analyzes 229 research papers from 2017 to 2023 to understand the role of LLMs in SE tasks. Different architectures like encoder-only, encoder-decoder, and decoder-only LLMs are explored for their effectiveness in handling SE challenges. The research emphasizes the significance of data sources, including open-source datasets, collected datasets, constructed datasets, and industrial datasets. The analysis reveals a trend towards decoder-only LLMs for improved performance in SE applications.
Stats
Open-source datasets like HumanEval contain real-world Python problems. Collected datasets are sourced from platforms like Stack Overflow for specific research questions. Constructed datasets are manually annotated with bug types for automated program repair studies. Industrial datasets from entities like China Merchants Bank offer real-world business scenarios.
Quotes
"Data is an indispensable factor in training Large Language Models (LLMs), determining their generality and effectiveness." - [300] "The choice between dataset types should be guided by specific research requirements and constraints." - [169]

Key Insights Distilled From

by Xinyi Hou,Ya... at arxiv.org 03-12-2024

https://arxiv.org/pdf/2308.10620.pdf
Large Language Models for Software Engineering

Deeper Inquiries

How can the use of industrial datasets enhance the applicability of Large Language Models (LLMs) in real-world scenarios?

Industrial datasets offer a unique advantage in enhancing the applicability of LLMs in real-world scenarios by providing access to proprietary business data, user behavior logs, and other sensitive information. These datasets contain valuable insights into real-world business operations and challenges, allowing LLMs to learn from actual industry-specific scenarios. By training LLMs on industrial datasets, researchers can ensure that the models are exposed to relevant and practical data that closely mirrors the complexities of real-world applications. This exposure helps LLMs better understand industry-specific language, patterns, and nuances, enabling them to make more accurate predictions and generate meaningful outputs tailored to specific industrial contexts.

How do different dataset types influence the architecture selection and performance of Large Language Models (LLMs) in Software Engineering?

Different dataset types play a crucial role in influencing both architecture selection and performance of LLMs in Software Engineering tasks. The choice of dataset type directly impacts how well an LLM can extract implicit features from the data and make informed decisions during processing. Code-based Datasets: Code-based datasets provide source code for training LLMs on software engineering tasks such as code comprehension or generation. Text-based Datasets: Text-based datasets focus on natural language text related to software engineering tasks like bug fixing or documentation generation. Graph-based Datasets: Graph-based datasets represent relationships between entities within software systems, aiding tasks like program analysis or dependency detection. Software Repository-based Datasets: Software repository data includes version control histories or issue tracking records for tasks like defect prediction or change impact analysis. Combined Data Types: Combined data types integrate multiple sources (e.g., code snippets with associated comments), offering rich context for diverse SE tasks. The selection of dataset type influences which architectural design is most suitable for a given task - encoder-only for understanding textual content, encoder-decoder for translation-like tasks involving input-output pairs, decoder-only for sequential prediction needs. Additionally, different dataset types may require specific preprocessing steps tailored to their characteristics before being fed into an LLM model. Ultimately, choosing appropriate dataset types ensures that an LLM is trained effectively on relevant data representative of SE challenges it will encounter.

What challenges arise when fine-tuning models with constructed datasets tailored to specific functions?

Fine-tuning models with constructed datasets tailored to specific functions presents several challenges: Data Quality Assurance: Ensuring high-quality annotations or modifications are made accurately without introducing biases that could affect model performance. Dataset Size & Diversity: Constructed datasets may be limited in size compared to open-source ones; ensuring diversity within this smaller set becomes crucial. Annotation Consistency & Relevance: Maintaining consistency across annotations while ensuring they remain relevant to target functions requires meticulous manual effort. Overfitting Risk: Fine-tuning on highly specialized constructed sets may lead models towards overfitting if not balanced with generalization techniques during training. Generalizability Concerns: Models fine-tuned on narrowly focused constructed sets might struggle when faced with unseen variations outside their training scope. Addressing these challenges involves careful curation processes during dataset construction along with robust validation methods post-fine-tuning phase ensuring optimized model adaptability across various SE applications beyond initial function-specific constraints
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