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In this work, we propose a novel perspective to the problem of patch correctness assessment: a correct patch implements changes that "answer" to a problem posed by buggy behaviour.
Automated Patch Assessment for Program Repair at Scale
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Where were the repair ingredients for Defects4j bugs?
Deheng Yang, Kui Liu, Dongsun Kim, Anil Koyuncu, Kisub Kim, Haoye Tian, Yan Lei, Xiaoguang Mao, Jacques Klein, and Tegawendé F Bissyandé. 2021b · 2021
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Context-Aware Code Change Embedding for Better Patch Correctness Assessment
Bo Lin, Shangwen Wang, Ming Wen, and Xiaoguang Mao. 2022 · 2022
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Predicting Patch Correctness Based on the Similarity of Failing Test Cases
Haoye Tian, Yinghua Li, Weiguo Pian, Abdoul Kader Kabore, Kui Liu, Andrew Habib, Jacques Klein, and Tegawendé F Bissyandé. 2022a · 2022
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Haoye Tian, Kui Liu, Yinghua Li, Abdoul Kader Kaboré, Anil Koyuncu, Andrew Habib, Li Li, Junhao Wen, Jacques Klein, and Tegawendé F Bissyandé. 2022b · 2022
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Crex: Predicting patch correctness in automated repair of C programs through transfer learning of execution semantics
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