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Bug-Report–Driven Fault Localization: Industrial Benchmarking and Lessons Learned at ABB Robotics
Publication Type:
Conference/Workshop Paper
Venue:
International Conference on Evaluation and Assessment in Software Engineering 2026
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 whether artificial intelligence can support fault localization using only the natural-language content of bug reports. By relying exclusively 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 Västerås, Sweden, comprising approximately 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 frequency features consistently outperformed the fine-tuned language models on this dataset, while data augmentation substantially improved Random Forest performance. These findings challenge the assumption that transformer-based models universally outperform classical approaches in industrial contexts with domain-specific data. Overall, we demonstrated that historical bug reports can be systematically leveraged for text-based, artificial intelligence-assisted fault localization, providing a scalable, low-cost, and empirically grounded complement to traditional debugging practices in industry.
Bibtex
@inproceedings{Hall7386,
author = {Pernilla Hall and Anton Ununger and Riccardo Rubei and Alessio Bucaioni},
title = {Bug-Report–Driven Fault Localization: Industrial Benchmarking and Lessons Learned at ABB Robotics},
month = {June},
year = {2026},
booktitle = {International Conference on Evaluation and Assessment in Software Engineering 2026},
url = {http://www.es.mdu.se/publications/7386-}
}
