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Deep multi-metrics learning for mobile app defect prediction using code and process metrics.

Ahmed Abdu¹, Hakim A Abdo², Inam Ullah³

¹School of Information, Xi'an University of Finance and Economics, Xi'an, 710100, China.

Scientific Reports

|November 4, 2025

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This summary is machine-generated.

This study introduces a Deep Multi-Metrics Learning Model (DMLM) for accurate mobile app defect prediction. DMLM effectively combines code and process metrics, outperforming existing methods in both effort-aware and non-effort-aware scenarios.

Area of Science:

Software Engineering
Machine Learning
Mobile Application Development

Background:

Accurate defect prediction is crucial for efficient mobile app development and resource allocation.
Existing defect prediction models are limited by their reliance on isolated data sources, failing to capture the interplay of code and process metrics.
There is a need for advanced models that integrate diverse metrics for improved prediction accuracy.

Purpose of the Study:

To propose a novel Deep Multi-Metrics Learning Model (DMLM) for enhanced mobile app defect prediction.
To leverage both code metrics from the current version and process metrics from previous releases within a unified framework.
To evaluate the performance of DMLM against state-of-the-art approaches.

Main Methods:

Keywords:

Code metrics Deep neural network Mobile app defect prediction Process metrics

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Developed a Deep Multi-Metrics Learning Model (DMLM) integrating code and process metrics.

Utilized a deep convolutional neural network (CNN) to identify complex patterns within the combined metrics.

Conducted experiments on nine real-world Android applications from Git repositories.

Main Results:

DMLM significantly outperformed state-of-the-art approaches in non-effort-aware settings, showing superior Area Under the Curve (AUC), F1 scores, and Matthews correlation coefficient (MCC).
The DMLM model also demonstrated superior performance compared to baseline methods in effort-aware scenarios (PofB20).
The findings confirm the model's effectiveness in diverse prediction contexts.

Conclusions:

The proposed DMLM effectively enhances mobile app defect prediction by integrating code and process metrics.
DMLM offers a robust solution for improving resource allocation and software quality in mobile app development.
The study underscores the importance of multi-metric learning for accurate and reliable defect prediction.