App Review Driven Collaborative Bug Finding

1 code implementation • 7 Jan 2023 • Xunzhu Tang, Haoye Tian, Pingfan Kong, Kui Liu, Jacques Klein, Tegawendé F. Bissyande

Our novelty is that we guide the bug finding process by considering that existing bugs have been hinted within app reviews.

Predicting Patch Correctness Based on the Similarity of Failing Test Cases

1 code implementation • 28 Jul 2021 • Haoye Tian, Yinghua Li, Weiguo Pian, Abdoul Kader Kaboré, Kui Liu, Andrew Habib, Jacques Klein, Tegawendé F. Bissyande

Then, after collecting a large dataset of 1278 plausible patches (written by developers or generated by some 32 APR tools), we use BATS to predict correctness: BATS achieves an AUC between 0. 557 to 0. 718 and a recall between 0. 562 and 0. 854 in identifying correct patches.

Representation Learning