Leveraging Large Language Models for Software Model Completion: Results from Industrial and Public Datasets

Christof Tinnes, Alisa Welter, Sven Apel·June 25, 2024

Summary

This paper investigates the potential of large language models (LLMs) in software model completion during evolution, with a focus on an approach called RaMc. RaMc combines LLMs, model histories, and retrieval-augmented generation to address the lack of exhaustive edit operations and context-dependent suggestions. Experiments on industrial and public datasets demonstrate the effectiveness of these models, achieving up to 86.19% type-correct completions in real-world scenarios. The study highlights the benefits of LLMs in handling noisy context and real-time completion, comparing them to fine-tuning methods. It contributes to the understanding of applying LLMs in software engineering, identifies areas for future research, and emphasizes the need for enhanced task and domain knowledge for more accurate model completion. The research also explores the use of GPT-4 and other models in various software development tasks, such as model generation, collaborative architecture, and repair, using serialized change graphs and graph-based representations.

Key findings

2

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the issue of semantic deficiencies in leveraging large language models (LLMs) for software model completion . This problem involves scenarios where the language model lacks domain knowledge or requirements, leading to incomplete or incorrect model completions. The paper proposes strategies to enhance the approach by incorporating context knowledge, such as fine-tuning, providing requirements, or leveraging other project data in repositories . While the specific focus on semantic deficiencies in software model completion may not be entirely new, the paper contributes novel insights by exploring remedies to improve the accuracy and effectiveness of LLMs in this context .


What scientific hypothesis does this paper seek to validate?

I would be happy to help you with that. Please provide me with the title or some details about the paper you are referring to so I can assist you better.


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Leveraging Large Language Models for Software Model Completion: Results from Industrial and Public Datasets" proposes several innovative ideas, methods, and models in the domain of software model completion .

  1. Fine-Tuning Strategies: The paper suggests leveraging domain-specific fine-tuning as a remedy for semantic deficiencies in software model completion. By incorporating context knowledge such as requirements or task context, fine-tuning the language model can enhance accuracy .

  2. Comparison of Approaches: The study compares domain-specific fine-tuning with a retrieval-based approach (RaMc) to gain insights into their effectiveness for software model completion. It explores the impact of dataset properties, training specifics, and the number of fine-tuning epochs on model accuracy .

  3. Model Repair and Evolution: The paper delves into the area of model repair, where consistency-preserving edit operations are used to detect and recommend repair operations for inconsistencies in software models. It also discusses the evolution of models from repositories as a more reflective approach to real-world complexities .

  4. Evaluation Metrics: The research emphasizes the importance of systematic evaluation of Large Language Models (LLMs) for model completion. By controlling for confounding factors and focusing on core effectiveness, the study aims to benchmark the proposed approaches accurately .

  5. Dataset Utilization: The paper utilizes three datasets, including an Industry Dataset extracted from SysML models, to balance internal and external validity in the research. These datasets provide a foundation for evaluating the proposed methods in a real-world context .

  6. Threats to Validity: The study acknowledges several design choices that may impact the full potential of LLMs for software model completion. These include the definition of simple change graphs, serialization methods, domain knowledge provision, and the choice of the base LLM, highlighting areas for further exploration and improvement .

Overall, the paper introduces novel approaches such as fine-tuning strategies, model repair techniques, and systematic evaluation methods to enhance software model completion using Large Language Models, contributing valuable insights to the field of software engineering . The paper "Leveraging Large Language Models for Software Model Completion: Results from Industrial and Public Datasets" introduces several characteristics and advantages of its proposed methods compared to previous approaches in the domain of software model completion. Here is an analysis based on the details provided in the paper:

  1. Fine-Tuning for Domain-Specific Context: One key characteristic of the proposed method is the utilization of domain-specific fine-tuning to enhance software model completion accuracy. By incorporating context knowledge from the software domain, the fine-tuned models show improved performance compared to generic language models that lack this domain-specific information.

  2. Improved Semantic Understanding: The paper highlights that the fine-tuned models exhibit better semantic understanding of software artifacts, leading to more accurate completions. This characteristic sets the proposed approach apart from previous methods that may not have focused on domain-specific semantics in software modeling tasks.

  3. Comparison with Retrieval-Based Approaches: The study compares the effectiveness of domain-specific fine-tuning with a retrieval-based approach (RaMc) commonly used in software model completion tasks. By conducting a detailed analysis of the performance metrics and results, the paper provides insights into the advantages of fine-tuning strategies over retrieval-based methods in certain scenarios.

  4. Model Repair and Evolution Capabilities: Another characteristic of the proposed method is its focus on model repair and evolution in software modeling tasks. By detecting inconsistencies in software models and recommending repair operations, the approach offers a proactive way to maintain model consistency and integrity, which may not have been addressed comprehensively by previous methods.

  5. Systematic Evaluation Framework: The paper introduces a systematic evaluation framework for Large Language Models (LLMs) in software model completion tasks. By defining and controlling for various evaluation metrics and factors, the proposed approach ensures a more rigorous and comprehensive assessment of model accuracy and effectiveness compared to previous methods that may have lacked such a structured evaluation framework.

  6. Real-World Dataset Utilization: The utilization of both an Industry Dataset extracted from SysML models and public datasets in the study adds a practical dimension to the research. By evaluating the proposed methods on real-world software artifacts, the paper demonstrates the applicability and advantages of the approach in industrial settings, providing a more realistic assessment compared to previous methods that may have relied on synthetic or limited datasets.

Overall, the characteristics and advantages of the proposed methods in the paper include domain-specific fine-tuning, improved semantic understanding, comparison with retrieval-based approaches, model repair and evolution capabilities, a systematic evaluation framework, and real-world dataset utilization. These aspects collectively contribute to the advancement of software model completion tasks using Large Language Models, offering a more effective and context-aware approach compared to previous methods in the field.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field of software model completion. Noteworthy researchers in this area include Christof Tinnes, Alisa Welter, Sven Apel, Timo Kehrer, Abdullah M Alshanqiti, Reiko Heckel, and many others . These researchers have contributed to various aspects of software model completion, such as rule-based specifications of model transformations, deriving model editing operations from meta-models, and automatic change recommendation based on change histories.

The key to the solution mentioned in the paper involves leveraging large language models (LLMs) for software model completion. The approach focuses on retrieval-augmented generation, which combines retrieval-based methods with generative language models to provide correct completions for software models. By incorporating domain knowledge, fine-tuning strategies, and providing recommendations, the approach aims to address semantic deficiencies and improve the accuracy of model completions .


How were the experiments in the paper designed?

The experiments in the paper were designed as follows:

  • Four experiments were conducted, one for each research question, with a significance level of 𝛼 = 0.05 .
  • Experiment 1 (RQ 1) involved preprocessing datasets, generating training and testing samples, selecting 200 testing samples, and choosing between 1 to 12 few-shot samples for each testing sample .
  • Experiment 2 (RQ 2) focused on the relationship between the number of few-shot samples and correctness, conducting statistical tests across all datasets to analyze correctness based on the number of samples and types of changes .
  • Experiment 3 (RQ 3) aimed to understand the success and failure of retrieval-augmented generation in completing software models, analyzing successful completions with repeating patterns, complex refactorings, and project-specific concepts .

What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study on leveraging large language models for software model completion includes two real-world datasets: RepairVision and Industry, along with a synthetic Ecore dataset . The code used in the experiments, scripts, public datasets, and results are publicly available, as mentioned in the document .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that need to be verified. Here is an analysis based on the information provided in the document:

  1. Hypothesis 1: The experiments demonstrate that Large Language Models (LLMs) with retrieval-augmented generation can handle noisy training examples, leverage domain knowledge from pre-training, and adapt to project-specific concepts for software model completion . The findings show that the approach can provide correct completions for simple recurring patterns, complex refactorings, and even project-specific concepts inferred from few-shot examples . This indicates a strong alignment with Hypothesis 1.

  2. Hypothesis 3: The paper suggests that strategies to fuse the approach with context knowledge, such as fine-tuning, providing requirements or task context in the prompt, and leveraging other project data in repositories, could address semantic deficiencies in the completions . Additionally, providing a list of recommendations may help improve identified deficiencies . These proposed remedies support the need to enhance the approach to overcome semantic challenges, thus validating Hypothesis 3.

  3. Hypothesis 4: The experiments reveal that more fine-tuning epochs are beneficial for the average token accuracy, and diverse repositories increase the difficulty of software model completion . The findings also indicate that the edge sampling algorithm plays a crucial role in the accuracy of completions, with a dependency on the sampling procedure . This highlights the importance of fine-tuning and sampling strategies, aligning with Hypothesis 4.

In conclusion, the experiments and results in the paper provide strong empirical support for the scientific hypotheses under investigation. The findings validate the hypotheses related to the capabilities of LLMs for software model completion, strategies to address semantic deficiencies, and the impact of fine-tuning and sampling procedures on completion accuracy.


What are the contributions of this paper?

The paper "Leveraging Large Language Models for Software Model Completion: Results from Industrial and Public Datasets" makes several key contributions in the field of software model completion:

  • It proposes an approach called RaMc that utilizes large language models, model histories, and retrieval-augmented generation for model completion .
  • The paper evaluates the potential of large language models for model completion through experiments on three datasets, including an industrial application, a public open-source community dataset, and a controlled collection of simulated model repositories .
  • The findings indicate that large language models show promise in supporting software model evolution, achieving 62.30% semantically correct completions on real-world industrial data and up to 86.19% type-correct completions .
  • The general inference capabilities of large language models are highlighted as particularly beneficial when dealing with concepts that have few, noisy, or no examples at all .

What work can be continued in depth?

Work that can be continued in depth typically involves projects or tasks that require further analysis, research, or development. This could include scientific research, academic studies, technological advancements, creative projects, business strategies, and more. By delving deeper into the subject matter, one can gain a more comprehensive understanding and potentially make significant progress or discoveries.

Tables

4

Introduction
Background
Emergence of large language models in software engineering
Challenges in software model evolution and completion
Objective
To assess the potential of LLMs in software model completion
Evaluate RaMc's approach and its impact on type-correct completions
Method
Data Collection
Selection of industrial and public datasets
Creation of datasets with model histories and serialized change graphs
Data Preprocessing
Cleaning and formatting data for LLM input
Preparation of context and edit operations for model training
RaMc Approach
Integration of LLMs, model histories, and retrieval-augmented generation
Addressing limitations of exhaustive edit operations and context-dependent suggestions
Experiments and Evaluation
Performance metrics: type-correct completion rates
Comparison with fine-tuning methods
Real-world scenarios and noisy context handling
Results and Findings
Effectiveness of LLMs in software model completion (up to 86.19%)
Advantages in handling real-time and noisy context
Case studies on GPT-4 and other models in diverse tasks
Future Research Directions
Task and domain knowledge enhancement for improved accuracy
Limitations and potential improvements of LLMs in software engineering
Conclusion
Implications for software engineering practices
The role of LLMs in augmenting developer productivity
Open questions and opportunities for further research in the field
Basic info
papers
software engineering
artificial intelligence
Advanced features
Insights
What is the primary focus of the paper concerning large language models in software model completion?
What percentage of type-correct completions did the experiments achieve in real-world scenarios using LLMs?
How does the study compare LLMs to fine-tuning methods in handling noisy context and real-time completion?
How does RaMc combine LLMs, model histories, and retrieval-augmented generation in software evolution?

Leveraging Large Language Models for Software Model Completion: Results from Industrial and Public Datasets

Christof Tinnes, Alisa Welter, Sven Apel·June 25, 2024

Summary

This paper investigates the potential of large language models (LLMs) in software model completion during evolution, with a focus on an approach called RaMc. RaMc combines LLMs, model histories, and retrieval-augmented generation to address the lack of exhaustive edit operations and context-dependent suggestions. Experiments on industrial and public datasets demonstrate the effectiveness of these models, achieving up to 86.19% type-correct completions in real-world scenarios. The study highlights the benefits of LLMs in handling noisy context and real-time completion, comparing them to fine-tuning methods. It contributes to the understanding of applying LLMs in software engineering, identifies areas for future research, and emphasizes the need for enhanced task and domain knowledge for more accurate model completion. The research also explores the use of GPT-4 and other models in various software development tasks, such as model generation, collaborative architecture, and repair, using serialized change graphs and graph-based representations.
Mind map
Addressing limitations of exhaustive edit operations and context-dependent suggestions
Integration of LLMs, model histories, and retrieval-augmented generation
Limitations and potential improvements of LLMs in software engineering
Task and domain knowledge enhancement for improved accuracy
Real-world scenarios and noisy context handling
Comparison with fine-tuning methods
Performance metrics: type-correct completion rates
RaMc Approach
Creation of datasets with model histories and serialized change graphs
Selection of industrial and public datasets
Evaluate RaMc's approach and its impact on type-correct completions
To assess the potential of LLMs in software model completion
Challenges in software model evolution and completion
Emergence of large language models in software engineering
Open questions and opportunities for further research in the field
The role of LLMs in augmenting developer productivity
Implications for software engineering practices
Future Research Directions
Experiments and Evaluation
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Results and Findings
Method
Introduction
Outline
Introduction
Background
Emergence of large language models in software engineering
Challenges in software model evolution and completion
Objective
To assess the potential of LLMs in software model completion
Evaluate RaMc's approach and its impact on type-correct completions
Method
Data Collection
Selection of industrial and public datasets
Creation of datasets with model histories and serialized change graphs
Data Preprocessing
Cleaning and formatting data for LLM input
Preparation of context and edit operations for model training
RaMc Approach
Integration of LLMs, model histories, and retrieval-augmented generation
Addressing limitations of exhaustive edit operations and context-dependent suggestions
Experiments and Evaluation
Performance metrics: type-correct completion rates
Comparison with fine-tuning methods
Real-world scenarios and noisy context handling
Results and Findings
Effectiveness of LLMs in software model completion (up to 86.19%)
Advantages in handling real-time and noisy context
Case studies on GPT-4 and other models in diverse tasks
Future Research Directions
Task and domain knowledge enhancement for improved accuracy
Limitations and potential improvements of LLMs in software engineering
Conclusion
Implications for software engineering practices
The role of LLMs in augmenting developer productivity
Open questions and opportunities for further research in the field
Key findings
2

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the issue of semantic deficiencies in leveraging large language models (LLMs) for software model completion . This problem involves scenarios where the language model lacks domain knowledge or requirements, leading to incomplete or incorrect model completions. The paper proposes strategies to enhance the approach by incorporating context knowledge, such as fine-tuning, providing requirements, or leveraging other project data in repositories . While the specific focus on semantic deficiencies in software model completion may not be entirely new, the paper contributes novel insights by exploring remedies to improve the accuracy and effectiveness of LLMs in this context .


What scientific hypothesis does this paper seek to validate?

I would be happy to help you with that. Please provide me with the title or some details about the paper you are referring to so I can assist you better.


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Leveraging Large Language Models for Software Model Completion: Results from Industrial and Public Datasets" proposes several innovative ideas, methods, and models in the domain of software model completion .

  1. Fine-Tuning Strategies: The paper suggests leveraging domain-specific fine-tuning as a remedy for semantic deficiencies in software model completion. By incorporating context knowledge such as requirements or task context, fine-tuning the language model can enhance accuracy .

  2. Comparison of Approaches: The study compares domain-specific fine-tuning with a retrieval-based approach (RaMc) to gain insights into their effectiveness for software model completion. It explores the impact of dataset properties, training specifics, and the number of fine-tuning epochs on model accuracy .

  3. Model Repair and Evolution: The paper delves into the area of model repair, where consistency-preserving edit operations are used to detect and recommend repair operations for inconsistencies in software models. It also discusses the evolution of models from repositories as a more reflective approach to real-world complexities .

  4. Evaluation Metrics: The research emphasizes the importance of systematic evaluation of Large Language Models (LLMs) for model completion. By controlling for confounding factors and focusing on core effectiveness, the study aims to benchmark the proposed approaches accurately .

  5. Dataset Utilization: The paper utilizes three datasets, including an Industry Dataset extracted from SysML models, to balance internal and external validity in the research. These datasets provide a foundation for evaluating the proposed methods in a real-world context .

  6. Threats to Validity: The study acknowledges several design choices that may impact the full potential of LLMs for software model completion. These include the definition of simple change graphs, serialization methods, domain knowledge provision, and the choice of the base LLM, highlighting areas for further exploration and improvement .

Overall, the paper introduces novel approaches such as fine-tuning strategies, model repair techniques, and systematic evaluation methods to enhance software model completion using Large Language Models, contributing valuable insights to the field of software engineering . The paper "Leveraging Large Language Models for Software Model Completion: Results from Industrial and Public Datasets" introduces several characteristics and advantages of its proposed methods compared to previous approaches in the domain of software model completion. Here is an analysis based on the details provided in the paper:

  1. Fine-Tuning for Domain-Specific Context: One key characteristic of the proposed method is the utilization of domain-specific fine-tuning to enhance software model completion accuracy. By incorporating context knowledge from the software domain, the fine-tuned models show improved performance compared to generic language models that lack this domain-specific information.

  2. Improved Semantic Understanding: The paper highlights that the fine-tuned models exhibit better semantic understanding of software artifacts, leading to more accurate completions. This characteristic sets the proposed approach apart from previous methods that may not have focused on domain-specific semantics in software modeling tasks.

  3. Comparison with Retrieval-Based Approaches: The study compares the effectiveness of domain-specific fine-tuning with a retrieval-based approach (RaMc) commonly used in software model completion tasks. By conducting a detailed analysis of the performance metrics and results, the paper provides insights into the advantages of fine-tuning strategies over retrieval-based methods in certain scenarios.

  4. Model Repair and Evolution Capabilities: Another characteristic of the proposed method is its focus on model repair and evolution in software modeling tasks. By detecting inconsistencies in software models and recommending repair operations, the approach offers a proactive way to maintain model consistency and integrity, which may not have been addressed comprehensively by previous methods.

  5. Systematic Evaluation Framework: The paper introduces a systematic evaluation framework for Large Language Models (LLMs) in software model completion tasks. By defining and controlling for various evaluation metrics and factors, the proposed approach ensures a more rigorous and comprehensive assessment of model accuracy and effectiveness compared to previous methods that may have lacked such a structured evaluation framework.

  6. Real-World Dataset Utilization: The utilization of both an Industry Dataset extracted from SysML models and public datasets in the study adds a practical dimension to the research. By evaluating the proposed methods on real-world software artifacts, the paper demonstrates the applicability and advantages of the approach in industrial settings, providing a more realistic assessment compared to previous methods that may have relied on synthetic or limited datasets.

Overall, the characteristics and advantages of the proposed methods in the paper include domain-specific fine-tuning, improved semantic understanding, comparison with retrieval-based approaches, model repair and evolution capabilities, a systematic evaluation framework, and real-world dataset utilization. These aspects collectively contribute to the advancement of software model completion tasks using Large Language Models, offering a more effective and context-aware approach compared to previous methods in the field.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field of software model completion. Noteworthy researchers in this area include Christof Tinnes, Alisa Welter, Sven Apel, Timo Kehrer, Abdullah M Alshanqiti, Reiko Heckel, and many others . These researchers have contributed to various aspects of software model completion, such as rule-based specifications of model transformations, deriving model editing operations from meta-models, and automatic change recommendation based on change histories.

The key to the solution mentioned in the paper involves leveraging large language models (LLMs) for software model completion. The approach focuses on retrieval-augmented generation, which combines retrieval-based methods with generative language models to provide correct completions for software models. By incorporating domain knowledge, fine-tuning strategies, and providing recommendations, the approach aims to address semantic deficiencies and improve the accuracy of model completions .


How were the experiments in the paper designed?

The experiments in the paper were designed as follows:

  • Four experiments were conducted, one for each research question, with a significance level of 𝛼 = 0.05 .
  • Experiment 1 (RQ 1) involved preprocessing datasets, generating training and testing samples, selecting 200 testing samples, and choosing between 1 to 12 few-shot samples for each testing sample .
  • Experiment 2 (RQ 2) focused on the relationship between the number of few-shot samples and correctness, conducting statistical tests across all datasets to analyze correctness based on the number of samples and types of changes .
  • Experiment 3 (RQ 3) aimed to understand the success and failure of retrieval-augmented generation in completing software models, analyzing successful completions with repeating patterns, complex refactorings, and project-specific concepts .

What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study on leveraging large language models for software model completion includes two real-world datasets: RepairVision and Industry, along with a synthetic Ecore dataset . The code used in the experiments, scripts, public datasets, and results are publicly available, as mentioned in the document .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that need to be verified. Here is an analysis based on the information provided in the document:

  1. Hypothesis 1: The experiments demonstrate that Large Language Models (LLMs) with retrieval-augmented generation can handle noisy training examples, leverage domain knowledge from pre-training, and adapt to project-specific concepts for software model completion . The findings show that the approach can provide correct completions for simple recurring patterns, complex refactorings, and even project-specific concepts inferred from few-shot examples . This indicates a strong alignment with Hypothesis 1.

  2. Hypothesis 3: The paper suggests that strategies to fuse the approach with context knowledge, such as fine-tuning, providing requirements or task context in the prompt, and leveraging other project data in repositories, could address semantic deficiencies in the completions . Additionally, providing a list of recommendations may help improve identified deficiencies . These proposed remedies support the need to enhance the approach to overcome semantic challenges, thus validating Hypothesis 3.

  3. Hypothesis 4: The experiments reveal that more fine-tuning epochs are beneficial for the average token accuracy, and diverse repositories increase the difficulty of software model completion . The findings also indicate that the edge sampling algorithm plays a crucial role in the accuracy of completions, with a dependency on the sampling procedure . This highlights the importance of fine-tuning and sampling strategies, aligning with Hypothesis 4.

In conclusion, the experiments and results in the paper provide strong empirical support for the scientific hypotheses under investigation. The findings validate the hypotheses related to the capabilities of LLMs for software model completion, strategies to address semantic deficiencies, and the impact of fine-tuning and sampling procedures on completion accuracy.


What are the contributions of this paper?

The paper "Leveraging Large Language Models for Software Model Completion: Results from Industrial and Public Datasets" makes several key contributions in the field of software model completion:

  • It proposes an approach called RaMc that utilizes large language models, model histories, and retrieval-augmented generation for model completion .
  • The paper evaluates the potential of large language models for model completion through experiments on three datasets, including an industrial application, a public open-source community dataset, and a controlled collection of simulated model repositories .
  • The findings indicate that large language models show promise in supporting software model evolution, achieving 62.30% semantically correct completions on real-world industrial data and up to 86.19% type-correct completions .
  • The general inference capabilities of large language models are highlighted as particularly beneficial when dealing with concepts that have few, noisy, or no examples at all .

What work can be continued in depth?

Work that can be continued in depth typically involves projects or tasks that require further analysis, research, or development. This could include scientific research, academic studies, technological advancements, creative projects, business strategies, and more. By delving deeper into the subject matter, one can gain a more comprehensive understanding and potentially make significant progress or discoveries.

Tables
4
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