References of "Tian, Haoye 50038509"
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See detailPredicting Patch Correctness Based on the Similarity of Failing Test Cases
Tian, Haoye UL; Li, Yinghua UL; Pian, Weiguo UL et al

in ACM Transactions on Software Engineering and Methodology (2022)

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See detailIs this Change the Answer to that Problem? Correlating Descriptions of Bug and Code Changes for Evaluating Patch Correctness
Tian, Haoye UL; Tang, Xunzhu UL; Habib, Andrew UL et al

in Is this Change the Answer to that Problem? Correlating Descriptions of Bug and Code Changes for Evaluating Patch Correctness (2022)

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See detailWhere were the repair ingredients for Defects4j bugs?
Yang, Deheng; Liu, Kui; Kim, Dongsun et al

in Empirical Software Engineering (2021), 26(6), 1--33

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See detailEvaluating Representation Learning of Code Changes for Predicting Patch Correctness in Program Repair
Tian, Haoye UL; Liu, Kui UL; Kabore, Abdoul Kader UL et al

in Tian, Haoye (Ed.) 35th IEEE/ACM International Conference on Automated Software Engineering, September 21-25, 2020, Melbourne, Australia (2020)

A large body of the literature of automated program repair develops approaches where patches are generated to be validated against an oracle (e.g., a test suite). Because such an oracle can be imperfect ... [more ▼]

A large body of the literature of automated program repair develops approaches where patches are generated to be validated against an oracle (e.g., a test suite). Because such an oracle can be imperfect, the generated patches, although validated by the oracle, may actually be incorrect. While the state of the art explore research directions that require dynamic information or rely on manually-crafted heuristics, we study the benefit of learning code representations to learn deep features that may encode the properties of patch correctness. Our work mainly investigates different representation learning approaches for code changes to derive embeddings that are amenable to similarity computations. We report on findings based on embeddings produced by pre-trained and re-trained neural networks. Experimental results demonstrate the potential of embeddings to empower learning algorithms in reasoning about patch correctness: a machine learning predictor with BERT transformer-based embeddings... [less ▲]

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See detailElectric-Field Control of Magnetization, Jahn-Teller Distortion, and Orbital Ordering in Ferroelectric Ferromagnets
Chen, Lan; Xu, Changsong; Tian, Haoye UL et al

in PHYSICAL REVIEW LETTERS (2019), 122(24),

Detailed reference viewed: 98 (20 UL)