Enterprise AI Analysis
Bug-Report-Driven Fault Localization: Industrial Benchmarking and Lessons Learned at ABB Robotics
This report details an industrial study on AI-assisted fault localization using natural-language bug reports at ABB Robotics. We compared traditional ML models (Logistic Regression, SVM, Random Forest) with fine-tuned transformer models (RoBERTa-Base, Distil-RoBERTa). Key findings include that traditional TF-IDF-based models outperformed transformer models, especially when data augmentation was applied. The approach provides a scalable, cost-effective way to narrow down the search space for developers during maintenance, without needing access to source code or execution traces. It's a pragmatic solution for industrial contexts with domain-specific, confidential data.
Quantifiable Impact for Your Enterprise
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Reduction in Debugging Time
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Increase in Triage Accuracy
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Faster Resolution Cycle
Deep Analysis & Enterprise Applications
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Methodology
Key Findings
Industrial Applicability
The study employed a six-stage pipeline: data collection, preprocessing, feature extraction, model selection, training, and evaluation. This involved using proprietary data from ABB Robotics, normalizing bug report text, and preparing features for both traditional ML (TF-IDF, sentence embeddings) and transformer models (tokenization). Models were trained and tuned on partitioned datasets, with a focus on mitigating class imbalance through augmentation and using ranking-centric metrics for evaluation. This systematic approach ensured a robust comparison under industrial constraints.
Traditional ML models (LR, SVM, RF) with TF-IDF features consistently outperformed fine-tuned transformer models (RoBERTa-Base, Distil-ROBERTa) on non-augmented data. Data augmentation significantly improved Random Forest performance. TF-IDF features, leveraging domain-specific terminology, proved more predictive than generalized semantic embeddings. The models achieved Top-1 accuracy around 0.53 and Top-5 accuracy up to 0.86, demonstrating their practical value in narrowing developers' search space for fault localization in industrial settings.
The text-only approach requires no source code, execution traces, or static analysis artifacts, making it directly deployable within existing industrial maintenance workflows. Lightweight, locally runnable TF-IDF models are effective and cost-efficient for early-hit component ranking. The findings support integrating ranked predictions into bug trackers or CI dashboards to assist triage, with periodic retraining ensuring reliability. This provides a scalable, low-cost, and empirically grounded complement to traditional debugging practices.
0.5263
Top-1 Accuracy Achieved by Best Model (LR+TF-IDF)
Enterprise Process Flow
Data Collection (5 years of bug reports)
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Preprocessing (text normalization)
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Feature Extraction (TF-IDF/Embeddings)
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Model Training (LR, SVM, RF, RoBERTa)
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Evaluation (Ranking Metrics)
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Fault Localization (Component Level)
| Metric | LR+TF-IDF (RS Full) | SVM+TF-IDF (Original) | RF+TF-IDF (SR Full) |
|---|---|---|---|
| Top-1 Acc. | 0.5263 | 0.5000 | 0.5263 |
| Top-5 Acc. | 0.8421 | 0.8553 | 0.7895 |
| Recall@1 | 0.4178 | 0.4090 | 0.4265 |
| MAP | 0.6109 | 0.6103 | 0.6171 |
ABB Robotics Implementation
The study utilized proprietary data from ABB Robotics in Västerås, Sweden, encompassing approximately five years of resolved industrial bug reports. Each report was linked to its verified code fix, allowing for robust supervised learning. This real-world dataset, with its inherent confidentiality constraints and label imbalance, provided a realistic context to assess the models' effectiveness and generalization under industrial conditions, confirming the practical applicability of the proposed text-only fault localization approach.
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Estimated Annual Savings
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Your AI Implementation Roadmap
A structured approach to integrate AI-driven fault localization into your existing workflows.
Phase 01: Data Assessment & Preparation
Initial audit of existing bug reporting systems and historical data. Establish secure data pipelines and preprocessing routines to ensure data quality and privacy.
Phase 02: Model Customization & Training
Select and fine-tune machine learning models using your proprietary datasets. Develop robust evaluation frameworks to measure performance against key metrics.
Phase 03: Integration & Pilot Deployment
Seamlessly integrate the AI fault localization tool into your current bug tracking and CI/CD systems. Conduct a pilot program with a subset of your development team to gather feedback.
Phase 04: Scaling & Continuous Improvement
Roll out the solution across relevant teams and continuously monitor its performance. Implement feedback loops for model retraining and adaptation to evolving codebases and reporting practices.
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