Statistical-Based Metric Threshold Setting Method for Software Fault Prediction in Firmware Projects: An Industrial Experience

TL;DR

This paper introduces a statistical metric threshold method for fault prediction in embedded firmware, enabling industry-relevant, interpretable, cross-project fault detection without machine learning models.

Contribution

It presents a structured, statistically-based approach for defining software metric thresholds that can be reused across projects, enhancing fault prediction interpretability in industrial firmware development.

Findings

Thresholds effectively distinguish faulty functions with high precision.

Method supports cross-project fault prediction without retraining.

Provides an interpretable alternative to black-box AI models.

Abstract

Ensuring software quality in embedded firmware is critical, especially in safety-critical domains where compliance with functional safety standards (ISO 26262) requires strong guarantees of software reliability. While machine learning-based fault prediction models have demonstrated high accuracy, their lack of interpretability limits their adoption in industrial settings. Developers need actionable insights that can be directly employed in software quality assurance processes and guide defect mitigation strategies. In this paper, we present a structured process for defining context-specific software metric thresholds suitable for integration into fault detection workflows in industrial settings. Our approach supports cross-project fault prediction by deriving thresholds from one set of projects and applying them to independently developed firmware, thereby enabling reuse across similar software systems without retraining or domain-specific tuning. We analyze three real-world C-embedded firmware projects provided by an industrial partner, using Coverity and Understand static analysis tools to extract software metrics. Through statistical analysis and hypothesis testing, we identify discriminative metrics and derived empirical threshold values capable of distinguishing faulty from non-faulty functions. The derived thresholds are validated through an experimental evaluation, demonstrating their effectiveness in identifying fault-prone functions with high precision. The results confirm that the derived thresholds can serve as an interpretable solution for fault prediction, aligning with industry standards and SQA practices. This approach provides a practical alternative to black-box AI models, allowing developers to systematically assess software quality, take preventive actions, and integrate metric-based fault prediction into industrial development workflows to mitigate software faults.