Papers with defect detection
GeoGround: Uncertainty-Weighted Multi-Task Learning for Geo-Alignment and Address Defect Detection (2026.acl-industry)
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| Challenge: | Address intelligence in e-commerce requires precise geocoding and proactive defect detection under strict sub-50 ms latency constraints. |
| Approach: | They propose a multi-task learning framework that jointly models coordinate grounding and address defect detection. |
| Outcome: | The proposed model achieves 5.86 gains in address defect detection precision and 4.86 improvements in location prediction accuracy over strong encoder baselines while remaining 75 more efficient than decoder LLMs such as Qwen2-1.5B. |
Addressing Leakage in Self-Supervised Contextualized Code Retrieval (2022.coling-1)
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| Challenge: | a recent study addresses the use of contextualized code retrieval to fill gaps in a partial input program. |
| Approach: | They propose a self-supervised approach to contextualized code retrieval . they propose mutual identifier masking, dedentation, and the selection of syntax-aligned targets . |
| Outcome: | The proposed approach improves retrieval substantially and yields state-of-the-art results for code clone and defect detection. |
Sibylvariant Transformations for Robust Text Classification (2022.findings-acl)
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| Challenge: | Existing text transformation techniques are limited in their ability to expand input space . many techniques can artificially expand labeled training sets or test suites, but are class-preserving . |
| Approach: | They propose a concept of sibylvariance to describe transforms that relax the label-preserving constraint and knowably vary the expected class. |
| Outcome: | The proposed transforms can expand input space, but they are limited in their ability to expand . the proposed transform can knowably vary the expected class and lead to more diverse distributions . |
Code Defect Detection Using Pre-trained Language Models with Encoder-Decoder via Line-Level Defect Localization (2024.lrec-main)
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| Challenge: | Recent code Pre-trained Language Models (PLMs) have shown great success in code defect detection tasks. |
| Approach: | They propose a method that integrates line-level defect localization into a unified training process to identify which lines contain defects. |
| Outcome: | The proposed method significantly improves performance on four benchmark datasets for code defect detection. |