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
pandangan teks lengkap
79
Ujian kombinatorial ialah teknik ujian yang berkesan untuk mengesan kerosakan dalam sistem perisian atau perkakasan dengan pelbagai faktor menggunakan kaedah gabungan. Dengan melakukan ujian, iaitu penetapan nilai yang mungkin kepada semua faktor, dan mengesahkan sama ada sistem berfungsi seperti yang diharapkan (lulus) atau tidak (gagal), kehadiran kerosakan boleh dikesan. Kegagalan ujian mungkin disebabkan oleh gabungan pelbagai faktor yang diberikan dengan nilai tertentu, dipanggil interaksi yang rosak. Martínez et al. [1] mencadangkan algoritma penyesuaian deterministik pertama untuk menemui interaksi yang rosak melibatkan paling banyak dua faktor di mana setiap faktor mempunyai dua nilai, yang mana perwakilan graf diguna pakai. Dalam makalah ini, kami menambah baik algoritma Martínez et al. dengan pendekatan algoritma penyesuaian untuk menemui interaksi yang rosak dalam apa yang dipanggil graf "bukan-2-boleh lokasi". Kami menunjukkan bahawa, untuk mana-mana sistem yang setiap "komponen faktor bukan 2 boleh lokasi" melibatkan dua interaksi yang rosak (contohnya, sistem yang mempunyai paling banyak dua interaksi yang rosak), algoritma kami yang dipertingkatkan dengan cekap menemui semua interaksi yang rosak dengan nilai yang sangat rendah. kebarangkalian tersilap disebabkan oleh proses pemilihan rawak dalam algoritma Martínez et al. Keberkesanan algoritma kami yang lebih baik didedahkan oleh kedua-dua perbincangan teori dan penilaian eksperimen.
Qianqian YANG
Hangzhou Dianzi University,University of Yamanashi
Xiao-Nan LU
University of Yamanashi
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Salinan
Qianqian YANG, Xiao-Nan LU, "An Improved Adaptive Algorithm for Locating Faulty Interactions in Combinatorial Testing" in IEICE TRANSACTIONS on Fundamentals,
vol. E105-A, no. 6, pp. 930-942, June 2022, doi: 10.1587/transfun.2021EAP1071.
Abstract: Combinatorial testing is an effective testing technique for detecting faults in a software or hardware system with multiple factors using combinatorial methods. By performing a test, which is an assignment of possible values to all the factors, and verifying whether the system functions as expected (pass) or not (fail), the presence of faults can be detected. The failures of the tests are possibly caused by combinations of multiple factors assigned with specific values, called faulty interactions. Martínez et al. [1] proposed the first deterministic adaptive algorithm for discovering faulty interactions involving at most two factors where each factor has two values, for which graph representations are adopted. In this paper, we improve Martínez et al.'s algorithm by an adaptive algorithmic approach for discovering faulty interactions in the so-called “non-2-locatable” graphs. We show that, for any system where each “non-2-locatable factor-component” involves two faulty interactions (for example, a system having at most two faulty interactions), our improved algorithm efficiently discovers all the faulty interactions with an extremely low mistaken probability caused by the random selection process in Martínez et al.'s algorithm. The effectiveness of our improved algorithm are revealed by both theoretical discussions and experimental evaluations.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2021EAP1071/_p
Salinan
@ARTICLE{e105-a_6_930,
author={Qianqian YANG, Xiao-Nan LU, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={An Improved Adaptive Algorithm for Locating Faulty Interactions in Combinatorial Testing},
year={2022},
volume={E105-A},
number={6},
pages={930-942},
abstract={Combinatorial testing is an effective testing technique for detecting faults in a software or hardware system with multiple factors using combinatorial methods. By performing a test, which is an assignment of possible values to all the factors, and verifying whether the system functions as expected (pass) or not (fail), the presence of faults can be detected. The failures of the tests are possibly caused by combinations of multiple factors assigned with specific values, called faulty interactions. Martínez et al. [1] proposed the first deterministic adaptive algorithm for discovering faulty interactions involving at most two factors where each factor has two values, for which graph representations are adopted. In this paper, we improve Martínez et al.'s algorithm by an adaptive algorithmic approach for discovering faulty interactions in the so-called “non-2-locatable” graphs. We show that, for any system where each “non-2-locatable factor-component” involves two faulty interactions (for example, a system having at most two faulty interactions), our improved algorithm efficiently discovers all the faulty interactions with an extremely low mistaken probability caused by the random selection process in Martínez et al.'s algorithm. The effectiveness of our improved algorithm are revealed by both theoretical discussions and experimental evaluations.},
keywords={},
doi={10.1587/transfun.2021EAP1071},
ISSN={1745-1337},
month={June},}
Salinan
TY - JOUR
TI - An Improved Adaptive Algorithm for Locating Faulty Interactions in Combinatorial Testing
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 930
EP - 942
AU - Qianqian YANG
AU - Xiao-Nan LU
PY - 2022
DO - 10.1587/transfun.2021EAP1071
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E105-A
IS - 6
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - June 2022
AB - Combinatorial testing is an effective testing technique for detecting faults in a software or hardware system with multiple factors using combinatorial methods. By performing a test, which is an assignment of possible values to all the factors, and verifying whether the system functions as expected (pass) or not (fail), the presence of faults can be detected. The failures of the tests are possibly caused by combinations of multiple factors assigned with specific values, called faulty interactions. Martínez et al. [1] proposed the first deterministic adaptive algorithm for discovering faulty interactions involving at most two factors where each factor has two values, for which graph representations are adopted. In this paper, we improve Martínez et al.'s algorithm by an adaptive algorithmic approach for discovering faulty interactions in the so-called “non-2-locatable” graphs. We show that, for any system where each “non-2-locatable factor-component” involves two faulty interactions (for example, a system having at most two faulty interactions), our improved algorithm efficiently discovers all the faulty interactions with an extremely low mistaken probability caused by the random selection process in Martínez et al.'s algorithm. The effectiveness of our improved algorithm are revealed by both theoretical discussions and experimental evaluations.
ER -