The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Dengan kerumitan dan skala perisian yang semakin meningkat, pengesanan dan pembaikan tingkah laku sesat pada peringkat awal adalah penting untuk mengurangkan kos pembangunan perisian. Dalam amalan penyetempatan kerosakan, proses biasa biasanya merangkumi tiga langkah: pelaksanaan kes ujian domain input, pembinaan vektor ujian domain model dan penilaian kecurigaan. Keberkesanan vektor ujian domain model adalah penting untuk mengesan kod yang rosak. Walau bagaimanapun, vektor ujian dengan label yang gagal biasanya menyumbang sebahagian kecil, yang pasti merendahkan keberkesanan penyetempatan kerosakan. Dalam makalah ini, kami mencadangkan kaedah penambahan data PVaug dengan menggunakan konteks perambatan kesalahan dan pengekod auto variasi (VAE). Keputusan empirikal kami pada 14 program menggambarkan bahawa PVaug telah mempromosikan keberkesanan penyetempatan kerosakan.
Zhuo ZHANG
Tianjin University
Donghui LI
Tianjin University
Lei XIA
No.83 Army Joint and Truma Disease Treatment Centre of PLA
Ya LI
Shanghai Jiaotong University
Xiankai MENG
Polytechnic University
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Salinan
Zhuo ZHANG, Donghui LI, Lei XIA, Ya LI, Xiankai MENG, "A Data Augmentation Method for Fault Localization with Fault Propagation Context and VAE" in IEICE TRANSACTIONS on Information,
vol. E107-D, no. 2, pp. 234-238, February 2024, doi: 10.1587/transinf.2023EDL8052.
Abstract: With the growing complexity and scale of software, detecting and repairing errant behaviors at an early stage are critical to reduce the cost of software development. In the practice of fault localization, a typical process usually includes three steps: execution of input domain test cases, construction of model domain test vectors and suspiciousness evaluation. The effectiveness of model domain test vectors is significant for locating the faulty code. However, test vectors with failing labels usually account for a small portion, which inevitably degrades the effectiveness of fault localization. In this paper, we propose a data augmentation method PVaug by using fault propagation context and variational autoencoder (VAE). Our empirical results on 14 programs illustrate that PVaug has promoted the effectiveness of fault localization.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2023EDL8052/_p
Salinan
@ARTICLE{e107-d_2_234,
author={Zhuo ZHANG, Donghui LI, Lei XIA, Ya LI, Xiankai MENG, },
journal={IEICE TRANSACTIONS on Information},
title={A Data Augmentation Method for Fault Localization with Fault Propagation Context and VAE},
year={2024},
volume={E107-D},
number={2},
pages={234-238},
abstract={With the growing complexity and scale of software, detecting and repairing errant behaviors at an early stage are critical to reduce the cost of software development. In the practice of fault localization, a typical process usually includes three steps: execution of input domain test cases, construction of model domain test vectors and suspiciousness evaluation. The effectiveness of model domain test vectors is significant for locating the faulty code. However, test vectors with failing labels usually account for a small portion, which inevitably degrades the effectiveness of fault localization. In this paper, we propose a data augmentation method PVaug by using fault propagation context and variational autoencoder (VAE). Our empirical results on 14 programs illustrate that PVaug has promoted the effectiveness of fault localization.},
keywords={},
doi={10.1587/transinf.2023EDL8052},
ISSN={1745-1361},
month={February},}
Salinan
TY - JOUR
TI - A Data Augmentation Method for Fault Localization with Fault Propagation Context and VAE
T2 - IEICE TRANSACTIONS on Information
SP - 234
EP - 238
AU - Zhuo ZHANG
AU - Donghui LI
AU - Lei XIA
AU - Ya LI
AU - Xiankai MENG
PY - 2024
DO - 10.1587/transinf.2023EDL8052
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E107-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2024
AB - With the growing complexity and scale of software, detecting and repairing errant behaviors at an early stage are critical to reduce the cost of software development. In the practice of fault localization, a typical process usually includes three steps: execution of input domain test cases, construction of model domain test vectors and suspiciousness evaluation. The effectiveness of model domain test vectors is significant for locating the faulty code. However, test vectors with failing labels usually account for a small portion, which inevitably degrades the effectiveness of fault localization. In this paper, we propose a data augmentation method PVaug by using fault propagation context and variational autoencoder (VAE). Our empirical results on 14 programs illustrate that PVaug has promoted the effectiveness of fault localization.
ER -