Enterprise AI Analysis
Is Fault Localization Effective on Industrial Software? A Case Study on Computer-Aided Engineering Projects
Fault localization (FL) methods have shown promise in general software but their effectiveness in specialized industrial software, like Computer-Aided Engineering (CAE) projects, remains underexplored. This study investigates the applicability and efficiency of state-of-the-art FL techniques on real-world CAE bugs, revealing significant challenges and proposing domain-specific improvements.
Authors: Zhilei Ren, Yue Ma, Xiaochen Li, Shikai Guo, and He Jiang
Executive Impact
Key metrics from our analysis highlight the current state and potential of AI-driven fault localization in industrial CAE projects.
0
Total Citations
234
Total Downloads
76
Real-World Bugs Analyzed
3
CAE Projects Studied
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
CAE Project Workflow
Input
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Pre-processing
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Solution
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Post-processing
Unique Challenges of CAE Software
CAE software is characterized by its iterative computing paradigm, requiring extensive and long-term computational efforts. Complex input parameters make test case generation difficult, and simulations can be prone to inaccuracies due to mesh quality and numerical algorithms. These factors significantly complicate fault localization compared to general software.
467.18
Avg. Statements to Check (DeepFL)
Even the best FL methods (DeepFL) require developers to examine hundreds of statements to find bugs in CAE projects, which is 23 times higher than what's needed for general-purpose software like Defects4J (20.32 statements).
208.13
Hours to Execute (MBFL/SBFL)
Mutation-based and Spectrum-based FL methods can take up to 208.13 hours for data collection in CAE projects, 10 to 62 times longer than in previous studies on smaller datasets like Defects4J due to large codebases and intensive iterative computations.
| Effectiveness of FL Methods in CAE Projects | |||
|---|---|---|---|
Analyzing FL methods at both statement and file levels shows DeepFL generally outperforms others in accuracy, though overall performance remains unsatisfactory compared to general software. Bugspots performs well at the file level due to main module modification frequency. | |||
| Metric | DeepFL (Statement) | DeepFL (File) | Bugspots (File) |
| MFR (Avg. Rank) | 467.18 | 54.03 | 4.03 |
| Top-1 Bugs Found | 0% | 1% | 55% |
| Top-20 Bugs Found | 0-3% | 14-25% | 94% |
| Impact of Bug Type on FL Effectiveness | ||
|---|---|---|
Multiple-file bugs in CAE projects (36.8%) are significantly harder to localize than single-file bugs. For MFEM and deal.II, MFR values increase when localizing multiple-file bugs, indicating more files must be checked. Rogot2 shows potential in multiple-file scenarios due to its reliance on comprehensive test coverage data. | ||
| Project (Bug Type) | DeepFL MFR | Rogot2 Top-1 (%) |
| MFEM (Single) | 53.00 | 0 |
| MFEM (Multiple) | 57.41 | 8 |
| deal.II (Single) | 69.52 | 0 |
| deal.II (Multiple) | 73.06 | 14 |
| FDS (Single) | 7.97 | 48 |
| FDS (Multiple) | 4.92 | 75 |
DeepFLmain: Leveraging Main Module Similarity for Enhanced FL
Our observation that most bugs are concentrated in 'main module' files, closely associated with core CAE functionalities, inspired the development of DeepFLmain. This enhanced method integrates a new feature: cosine similarity between source code files and their corresponding main module documentation. This domain-specific insight helps DeepFL to more accurately identify critical code areas prone to faults.
- Leverages domain-specific knowledge
- Identifies critical 'hotspot' modules
- Improves FL accuracy in CAE projects
35.93%
Mean Average Rank (MAR) Improvement
By integrating main module-based features, DeepFLmain improved the Mean Average Rank (MAR) of fault localization by 35.93% across CAE projects.
45.00%
Mean First Rank (MFR) Improvement
The Mean First Rank (MFR) of fault localization saw a significant improvement of 45.00% with DeepFLmain, indicating buggy elements are ranked higher in the suspiciousness list.
9.68%
Additional Top-1 Bugs Identified
DeepFLmain successfully identified an additional 9.68% of bugs in the Top-1 metric, demonstrating its practical value for developers.
| DeepFLmain Validation: Kratos Project Results | |||
|---|---|---|---|
Further validation on the Kratos project (a multi-disciplinary simulation framework) confirms the generalizability of DeepFLmain. It shows substantial improvements in MAR and MFR for both single-file and multiple-file bugs, with MFR increasing by 46.45% and 52.70% respectively. | |||
| Kratos Bug Type | DeepFL MFR | DeepFLmain MFR | MFR Improvement |
| Single-file bugs | 246.50 | 132.00 | 46.45% |
| Multiple-file bugs | 119.45 | 56.50 | 52.70% |
Calculate Your Potential AI ROI
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Annual Savings
$0
Hours Reclaimed Annually
0
Your AI Implementation Roadmap
A clear, phased approach to integrating advanced AI into your software development lifecycle for optimal fault localization.
Phase 01: Discovery & Strategy
Comprehensive assessment of your existing software architecture, bug data, and development workflows. Define clear objectives and a tailored AI integration strategy, including data requirements and expected ROI targets.
Phase 02: Data Preparation & Model Training
Collect and preprocess historical bug reports, code changes, and execution traces. Train custom DeepFLmain models using your domain-specific data, leveraging main module similarity features for enhanced accuracy.
Phase 03: Pilot Integration & Validation
Integrate the DeepFLmain system into a pilot CAE project. Conduct extensive testing and validation against real-world bugs, meticulously measuring improvements in MAR, MFR, and developer efficiency.
Phase 04: Full-Scale Deployment & Optimization
Roll out the AI fault localization across all relevant CAE projects. Continuously monitor performance, gather feedback, and iterate on model fine-tuning and feature enhancements to maximize long-term effectiveness and adapt to evolving software complexities.
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