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Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish

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Published on: March 6, 2014

Software defect prediction based on residual/shuffle network optimized by upgraded fish migration optimization

Zhijing Liu¹, Tong Su², Michail A Zakharov³

¹Institute of Innovation and Entrepreneurship, Shandong Huayu University of Technology, Dezhou, 253034, Shandong, China. 18253487107@163.com.

Scientific Reports

|February 28, 2025

Summary

This summary is machine-generated.

This study introduces a novel AI method using Residual/Shuffle Networks and Fish Migration Optimization for accurate software defect prediction. The approach significantly enhances defect detection accuracy and reduces manual effort in software development.

Keywords:

Deep learning Defect prediction Feature generation Residual-shuffle network Software defect prediction Upgraded fish migration optimization algorithm

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Area of Science:

Computer Science
Artificial Intelligence
Software Engineering

Background:

Software defects pose significant challenges, increasing development costs and impacting user satisfaction.
Existing defect prediction models often require substantial manual effort and may lack accuracy.
There is a need for advanced, automated methods to improve software quality.

Purpose of the Study:

To introduce a new, accurate method for predicting software defects.
To reduce the manual effort required in identifying software issues.
To leverage the synergy of deep learning and metaheuristics for code analysis.

Main Methods:

Utilized Residual/Shuffle (RS) Networks for deep learning-based code analysis.
Employed an enhanced Fish Migration Optimization (UFMO) algorithm for model training.
Extracted semantic and structural properties from software code.

Main Results:

Achieved an average accuracy of 93% on open-source projects.
Demonstrated superior performance compared to state-of-the-art models.
Reported significant improvements in precision (78-98%), recall (71-98%), F-measure (72-96%), and AUC (78-99%).

Conclusions:

The proposed model offers a simple, efficient, and effective solution for defect prediction.
This AI-driven approach can revolutionize software development by improving quality and reducing costs.
Further evaluation on proprietary software is recommended to broaden applicability.