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Topic modeling-based prediction of software defects and root cause using BERTopic, and multioutput classifier.

Devi Priya Gottumukkala¹, Prasad Reddy P V G D², S Krishna Rao³

¹Department of CS&SE, TDR-HUB, Andhra University, Visakhapatnam, India. mantena2377@gmail.com.

Scientific Reports

|July 14, 2025

Summary

This summary is machine-generated.

This study introduces BERT-MOC for software defect prediction (SDP), using natural language processing (NLP) and machine learning (ML). The model accurately predicts defects and their root causes, improving software engineering efficiency.

Keywords:

BERTopic Multioutput classifier Software defect prediction (SDP)

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Area of Science:

Software Engineering
Artificial Intelligence
Natural Language Processing

Background:

Software defects pose significant challenges in software engineering, leading to increased debugging and maintenance costs.
Existing software defect prediction (SDP) methods often lack the ability to identify the root cause of defects.
Advanced techniques in natural language processing (NLP) and machine learning (ML) offer new possibilities for improving SDP.

Purpose of the Study:

To develop an advanced methodology for software defect prediction (SDP) that simultaneously identifies defects and their root causes.
To leverage transformer-based topic modeling and multi-output classification for enhanced defect analysis.
To improve the efficiency and accuracy of defect resolution in software development.

Main Methods:

The proposed BERT-MOC methodology integrates BERTopic for topic modeling of defect descriptions and a multi-output classifier.
BERTopic extracts meaningful topic representations from textual defect data to identify defect root causes.
A multi-output classifier, utilizing estimators like Logistic Regression, Decision Tree, K-neighbor, Random Forest, and Ensemble Method-Voting, is trained on combined topic representations and defect logs.

Main Results:

The BERT-MOC model achieved high performance in predicting both defect presence and root cause.
The multi-output classifier with ensemble method voting demonstrated superior results, achieving 97% accuracy and F1-score for root cause prediction.
The model also attained 94% accuracy and F1-score for predicting defect or not defect.

Conclusions:

The BERT-MOC methodology offers a novel and effective approach to software defect prediction and root cause analysis.
Integrating NLP topic modeling with multi-output ML classification significantly enhances the accuracy of defect prediction.
This approach has the potential to substantially reduce debugging and maintenance efforts in software engineering.