An empirical assessment of different word embedding and deep learning models for bug assignment

Wang, Rongcun; Ji, Xingyu; Xu, Senlei; Tian, Yuan; Jiang, Shujuan; Huang, Rubing

doi:10.1016/j.jss.2024.111961

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

2025

Publication Types

Select...

Article2

Other1

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

DHG-BiGRU: Dual-attention based hierarchical gated BiGRU for software defect prediction

Malhotra,

Singh

2025

Information and Software Technology

View full text Add to dashboard Cite

DHG-BiGRU: Dual-attention based hierarchical gated BiGRU for software defect prediction

Malhotra,

Singh

2025

Information and Software Technology

View full text Add to dashboard Cite

Coding-PTMs: How to Find Optimal Code Pre-trained Models for Code Embedding in Vulnerability Detection?

Zhao,

Gong,

Huang

et al. 2024

Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering

View full text Add to dashboard Cite

Automatic Bug Triaging Process: An Enhanced Machine Learning Approach through Large Language Models

Chhabra,

Chadha

2024

Eng. Technol. Appl. Sci. Res.

View full text Add to dashboard Cite

Bug resolution and maintenance are the most critical phases of the software development life cycle. The traditional bug triaging concept refers to the manual assignment of bugs to the appropriate developer after reading the bug details from the bug tracker and further resolving it. The advent of machine learning algorithms provides various solutions for automated bug triaging. Machine learning algorithms can be used to classify bugs and assign each to a developer. Reducing manual efforts optimizes bug-triaging by utilizing manpower in other software development processes. Furthermore, machine learning Large Language Models (LLMs) can be used to take advantage of their natural language processing features and capabilities. This study proposes a machine learning-based embed chain LLM approach for automatic bug triaging. This approach is used to automatically classify bug reports. Based on the results, the appropriate developer is recommended. In addition, the proposed approach is used to automatically predict the priority of bug reports. This paper also discusses the strengths and challenges of the proposed approach.

show abstract

An empirical assessment of different word embedding and deep learning models for bug assignment

Cited by 3 publications

References 39 publications

DHG-BiGRU: Dual-attention based hierarchical gated BiGRU for software defect prediction

DHG-BiGRU: Dual-attention based hierarchical gated BiGRU for software defect prediction

Coding-PTMs: How to Find Optimal Code Pre-trained Models for Code Embedding in Vulnerability Detection?

Automatic Bug Triaging Process: An Enhanced Machine Learning Approach through Large Language Models

Contact Info

Product

Resources

About