Bug-Report-Driven Fault Localization: Industrial Benchmarking and Lesson Learned at ABB Robotics

TL;DR

This paper explores AI-based fault localization using only textual bug reports in industrial settings, comparing traditional machine learning models and transformer-based models on proprietary data from ABB Robotics.

Contribution

It demonstrates that classical models outperform transformer models in this domain, challenging assumptions about AI model effectiveness in industrial fault localization.

Findings

Traditional models outperform transformer models on industrial bug report data.

Data augmentation improves Random Forest performance.

Text-based AI methods can effectively support fault localization without source code.

Abstract

Software quality assurance remains a major challenge in industrial environments, where large-scale and long-lived systems inevitably accumulate defects. Identifying the location of a fault is often time-consuming and costly, particularly during maintenance phases when developers must rely primarily on textual bug reports rather than complete runtime or code-level context. In this study, we investigated if artificial intelligence can support fault localization using only the natural-language content of bug reports. By relying only on textual information, our approach requires no access to source code, execution traces, or static analysis artifacts, making it directly deployable within existing industrial maintenance workflows. We framed fault localization as a supervised text classification problem and evaluated three traditional machine learning models (Logistic Regression, Support Vector Machine, and Random Forest) and two fine-tuned transformer-based language models (RoBERTa-Base and Distil-RoBERTa). Our evaluation used proprietary data from ABB Robotics in Sweden, comprising five years of resolved industrial bug reports, each linked to its verified code fix. This setting allowed us to assess model effectiveness under realistic industrial constraints. Our results showed that traditional models using term frequency-inverse document features consistently outperformed the fine-tuned language models on this dataset, while data augmentation improved Random Forest performance. These findings challenge the assumption that transformer-based models universally outperform classical approaches in industrial contexts with domain-specific data. We demonstrated that historical bug reports can be systematically used for text-based, artificial intelligence-assisted fault localization, providing a scalable, low-cost, and empirically grounded complement to common debugging practices in industry.