A Machine Learning-Based Framework for Accurate Duplicate Bug Detection

Abstract

In modern software development environments, duplicate bug reports have become a significant challenge, leading to increased manual effort, delayed issue resolution, and reduced productivity. Developers often describe the same issue using different terminology, making manual identification of duplicates time-consuming and error-prone. In this paper, we present an efficient and scalable machine learning-based framework for duplicate bug detection using Natural Language Processing (NLP) techniques. The proposed system integrates text preprocessing methods such as tokenization, stopword removal, and normalization to enhance data quality. TF-IDF (Term Frequency–Inverse Document Frequency) is used for feature extraction, and cosine similarity is applied to measure the similarity between bug reports. The system assigns similarity scores and categorizes results into confidence levels to determine whether a bug is a duplicate or unique. A Streamlit-based interface is developed to enable real-time detection and user interaction with visual feedback. Experimental results demonstrate improved efficiency in identifying duplicate bug reports, reducing manual effort, and enhancing accuracy compared to traditional approaches. The proposed approach provides a reliable, efficient, and scalable solution for automated bug tracking and software maintenance in large-scale development environments.

Introduction

Duplicate bug reports are a major challenge in modern software development, as the growing number of reports from different users and developers makes issue management more complex. Traditional bug tracking methods rely on manual inspection, which is time-consuming, error-prone, and inefficient. Failure to identify duplicate bugs results in redundant work, increased development costs, and slower issue resolution. Variations in language, unstructured data, and semantic differences between bug descriptions further complicate duplicate detection.

To address these challenges, the proposed system uses Machine Learning (ML) and Natural Language Processing (NLP) techniques to automatically identify duplicate bug reports. The system applies text preprocessing methods such as text cleaning, tokenization, stopword removal, and normalization to improve data quality. It then uses TF-IDF vectorization to convert bug descriptions into numerical representations and cosine similarity to measure the similarity between reports. A Streamlit-based interface enables real-time detection and provides users with clear similarity scores and confidence levels.

The literature review highlights several machine learning approaches, including Gradient Boosting, Decision Trees, Random Forests, Logistic Regression, optimization-based models, and AutoML techniques. While these methods improve detection accuracy, they often face limitations such as high computational costs, overfitting, lack of interpretability, and difficulties in real-time deployment. Existing systems also struggle to capture semantic relationships between differently worded but similar bug reports.

The main objective of the study is to develop an efficient and scalable duplicate bug detection framework that improves accuracy, handles variations in textual descriptions, and supports real-time decision-making. Specific goals include implementing preprocessing techniques, applying feature extraction and similarity analysis, determining optimal similarity thresholds, and creating a user-friendly detection system.

The proposed system follows a structured workflow consisting of data collection, preprocessing, feature extraction, similarity computation, and prediction. Bug reports are processed using NLP techniques, transformed into TF-IDF vectors, and compared using cosine similarity. Based on similarity scores, reports are classified as High Match, Medium Match, Low Match, or No Match.

Key advantages of the system include high accuracy, scalability for large datasets, interpretability through confidence scores, and reliable performance across different reporting styles. By automating duplicate bug detection, the system reduces manual effort, improves issue tracking efficiency, supports faster bug resolution, and enhances overall software development productivity.

Conclusion

In this paper, we proposed a machine learning-based framework for accurate duplicate bug detection using textual bug report data, addressing the limitations of traditional manual bug tracking approaches. The system integrates preprocessing techniques, feature extraction, and similarity-based methods to improve detection accuracy and reliability. By automating the analysis of bug descriptions, the proposed method supports faster identification of duplicate issues and reduces dependency on manual inspection, thereby enhancing software maintenance processes and development efficiency. One of the key contributions of this work is the effective use of text preprocessing techniques along with TF-IDF vectorization and cosine similarity to improve detection performance. These techniques enhance the system’s ability to identify similar bug reports while maintaining balanced precision and recall. In addition, the deployment of the system through a Streamlit-based interface enables real-time detection and provides interpretable similarity results, making it practical and accessible for developers in real-world environments. The experimental results demonstrate that the proposed system achieves improved accuracy, efficiency, and reliability compared to traditional methods. The system shows strong generalization capability and consistent performance across evaluation metrics such as accuracy, precision, recall, and F1-score. Overall, the proposed approach offers a scalable, efficient, and reliable solution for duplicate bug detection, contributing to improved bug tracking processes and supporting faster issue resolution in software development workflows.

References

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Copyright

Copyright © 2026 V. Raja Manikanta, K. Prasanthi, Ch. Sandeep, J. Pawan Kumar, N. Suresh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.