Futures

Comparing BERT and ChatGPT4 Embeddings for Bug Report Similarity, from (20221217.)

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Summary

This study compares the effectiveness of different text embedding models, such as BERT and ChatGPT4, in retrieving similar bug reports based on similarity scores. The researchers used the Software Defects Data, which contains bug reports from various software projects, to evaluate the performance of these models. The experimental results show that BERT outperformed other models in terms of recall accuracy, followed by ChatGPT4, Gensim, FastText, and TFIDF. The study highlights the importance of selecting the appropriate embedding method for retrieving similar bug reports. The article also discusses the similarities between BERT and ChatGPT4 as NLP models and their application in tasks such as text classification and entity recognition.

Keywords

• BERT
• ChatGPT4
• semantic textual similarity
• bug reports
• embedding models
• Software Defects Data
• recall
• NLP models
• duplicate bug reports
• efficiency

Themes

• Text Embedding Models
• Bug Reports
• Semantic Text Similarity

Signals

Signal	Change	10y horizon	Driving force
Comparing effectiveness of semantic textual similarity methods	Improvement in bug report retrieval based on similarity score	More accurate retrieval of similar bug reports	Improving bug tracking workflows and efficiency

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