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Comparative Study of BERT and ChatGPT4 for Bug Report Similarity Retrieval, (from page 20221217.)

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Summary

This study compares the effectiveness of various semantic textual similarity methods in retrieving similar bug reports, focusing on embedding models like BERT and ChatGPT4. Utilizing the Software Defects Data set comprising approximately 480,000 bug reports, the results indicate that BERT outperforms other models, including ChatGPT4, Gensim, FastText, and TFIDF, particularly in recall accuracy. Different models were evaluated based on their efficiency in capturing sentence text similarity, with BERT consistently demonstrating superior performance. The study underscores the importance of selecting appropriate embedding methods for identifying duplicate bug reports, highlighting BERT’s strengths in improving bug tracking workflows.

Signals

name description change 10-year driving-force relevancy
Advancements in NLP Models Emerging NLP models are increasingly outperforming traditional methods in specific tasks. From reliance on traditional models to a preference for advanced NLP models like BERT. NLP models will dominate various fields, enhancing efficiency in tasks like bug tracking and content generation. The need for improved accuracy and efficiency in data processing tasks across industries. 4
Automated Bug Tracking Enhanced NLP models are automating the identification of duplicate bug reports. From manual identification of bugs to automated processes powered by advanced NLP models. Bug tracking systems will become fully automated, reducing human error and improving response times. The increasing volume of software projects necessitating efficient bug tracking solutions. 5
Importance of Semantic Understanding The significance of semantic understanding in text similarity tasks is gaining recognition. From basic keyword matching to sophisticated semantic analysis for text similarity. Text analysis will rely heavily on semantic understanding, transforming content retrieval and classification. The need for more precise information retrieval in complex datasets and diverse applications. 4
Data-Driven Model Selection Selection of embedding models is increasingly data-driven, optimizing for specific tasks. From generic model usage to tailored model selection based on performance metrics. Organizations will routinely customize their NLP model selection to optimize task-specific outcomes. The push for performance optimization in competitive software development environments. 3
Open Source Collaboration Availability of open-source datasets and models facilitates research and development in NLP. From proprietary solutions to collaborative, open-source approaches in NLP model development. The landscape of NLP research will be dominated by open-source initiatives and community collaboration. The drive for innovation and knowledge sharing in the tech community. 4

Concerns

name description relevancy
Data Privacy and Security The use of large datasets containing bug reports could expose sensitive information if not handled properly. 4
Model Bias and Fairness The performance of NLP models may vary based on the data they were trained on, leading to biased outcomes in bug detection. 3
Dependence on Pre-trained Models Relying heavily on pre-trained models without fine-tuning could result in suboptimal performance for specific tasks. 4
Resource Intensity of Model Training Training large models requires substantial computational resources, potentially limiting access for smaller organizations. 4
Performance Variability Different models may yield inconsistent results, raising concerns about reliability in automated bug detection. 3
Interpretability of Model Decisions The black-box nature of deep learning models makes it hard to understand how decisions are made regarding bug similarity. 5

Behaviors

name description relevancy
Comparative Performance Analysis of NLP Models Evaluating and comparing the effectiveness of various NLP models like BERT and ChatGPT4 for specific tasks, such as bug report identification. 5
Utilization of Large Datasets for Model Training Using extensive datasets, such as 480,000 bug reports, to enhance the training and accuracy of NLP models. 5
Automating Bug Report Identification Employing advanced NLP models to automate the process of identifying duplicate bug reports, improving efficiency in bug tracking. 4
Fine-tuning Embedding Models for Specific Tasks Fine-tuning models like Gensim for specific datasets to leverage their strengths for better performance in tasks like bug report retrieval. 4
Impact of Embedding Selection on Task Performance Highlighting how the choice of embedding model affects the performance in tasks related to semantic similarity and retrieval. 5
Use of Pre-trained Models in NLP Tasks Leveraging pre-trained models like BERT, Fasttext, and Doc2Vec to enhance the efficiency and accuracy of NLP applications. 4
Evaluation Metrics for NLP Model Comparison Establishing clear metrics such as recall and accuracy for comparing the performance of different NLP models in specific tasks. 5
Adoption of Nearest Neighbors Model in IR Using the Nearest Neighbors approach for Information Retrieval tasks, indicating a trend towards simpler, effective models for similarity matching. 3

Technologies

name description relevancy
BERT A pre-trained NLP model designed for understanding semantic meaning in text, outperforming others in bug report retrieval and similarity. 5
ChatGPT4 An advanced language model used for semantic understanding and text similarity tasks, capable of identifying duplicate bug reports effectively. 4
ADA An embedding model used for text search and similarity, showcasing competitive performance in bug report identification tasks. 4
FastText A word vector model providing embeddings trained on subword information, though less effective compared to BERT in this study. 3
Doc2Vec A model for generating vector representations of documents, applied in bug report analysis but with varying performance results. 3
Gensim An NLP library that includes fine-tuning capabilities for embedding models, used in the context of bug report retrieval. 3

Issues

name description relevancy
Performance of NLP Models in Software Engineering The comparative effectiveness of NLP models like BERT and ChatGPT4 in automating software bug report analysis. 4
Implications of Embedding Model Selection Choosing the right embedding model can significantly affect the accuracy of retrieving similar bug reports in software projects. 5
Scalability of Bug Tracking Systems The study highlights the need for scalable solutions in bug tracking as software projects continue to grow in complexity and size. 3
Advancements in Semantic Understanding Ongoing improvements in NLP models contribute to better semantic understanding in various applications, including software defect management. 4
Integration of AI in Software Development The increasing reliance on AI models for tasks such as bug detection may reshape workflows and practices in software development. 5
Dataset Diversity and Quality The quality and diversity of datasets used for training NLP models can impact their effectiveness in real-world applications. 4