Asymptotic Sequential Test for Order Determination

SHORT RANGE ORDER DETERMINATION IN AMORPHOUS FeGe2 WITH ANOMALOUS X-RAY SCATTERING

Le Journal de Physique Colloques ◽

10.1051/jphyscol:1985826 ◽

1985 ◽

Vol 46 (C8) ◽

pp. C8-187-C8-191

Author(s):

R. D. Lorentz ◽

A. I. Bienenstock

Keyword(s):

Short Range ◽

Short Range Order ◽

Range Order ◽

X Ray ◽

X Ray Scattering ◽

Order Determination ◽

Ray Scattering

Modeling System Audit as a Sequential test with Discovery as a Failure Time Endpoint

SSRN Electronic Journal ◽

10.2139/ssrn.3442169 ◽

2011 ◽

Author(s):

Craig S Wright ◽

Tanveer A. Zia

Keyword(s):

Failure Time ◽

Sequential Test

Low-cost Beamforming-based DOA Estimation with Model Order Determination

2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM) ◽

10.1109/sam48682.2020.9104347 ◽

2020 ◽

Author(s):

Elias Aboutanios ◽

Aboulnasr Hassanien

Keyword(s):

Low Cost ◽

Doa Estimation ◽

Model Order ◽

Model Order Determination ◽

Order Determination

Critical Values for a Sequential Test for Many Outliers

Journal of the Royal Statistical Society Series C (Applied Statistics) ◽

10.2307/2346808 ◽

1979 ◽

Vol 28 (1) ◽

pp. 36 ◽

Cited By ~ 16

Author(s):

P. Prescott

Keyword(s):

Critical Values ◽

Sequential Test

Recent advances in sequential test generation

Digest of Papers. 1992 IEEE VLSI Test Symposium ◽

10.1109/vtest.1992.232759 ◽

2003 ◽

Author(s):

Kwang-Ting Cheng

Keyword(s):

Test Generation ◽

Sequential Test ◽

Recent Advances

A shorter truncated sequential test

Microelectronics Reliability ◽

10.1016/0026-2714(71)90027-8 ◽

1971 ◽

Vol 10 (6) ◽

pp. 414

Keyword(s):

Sequential Test

Cumulant-based order determination of non-Gaussian ARMA models

IEEE Transactions on Acoustics Speech and Signal Processing ◽

10.1109/29.57576 ◽

1990 ◽

Vol 38 (8) ◽

pp. 1411-1423 ◽

Cited By ~ 141

Author(s):

G.B. Giannakis ◽

J.M. Mendel

Keyword(s):

Arma Models ◽

Non Gaussian ◽

Order Determination

Sensitivity analysis of the sequential test for detecting cyber-physical attacks

2015 23rd European Signal Processing Conference (EUSIPCO) ◽

10.1109/eusipco.2015.7362787 ◽

2015 ◽

Cited By ~ 3

Author(s):

Van Long Do ◽

Lionel Fillatre ◽

Igor Nikiforov

Keyword(s):

Sensitivity Analysis ◽

Sequential Test

Convolutive-Error-Measure Analysis for Inverse Filtering and Minimum Total-Model-Order Determination Subject to Positive-Definiteness

IEEE Journal of Selected Topics in Signal Processing ◽

10.1109/jstsp.2010.2048391 ◽

2010 ◽

Vol 4 (3) ◽

pp. 481-493

Author(s):

Shih Yu Chang ◽

Hsiao-Chun Wu

Keyword(s):

Positive Definiteness ◽

Inverse Filtering ◽

Model Order ◽

Error Measure ◽

Measure Analysis ◽

Total Model ◽

Model Order Determination ◽

Order Determination

Parallel Test Prioritization

ACM Transactions on Software Engineering and Methodology ◽

10.1145/3471906 ◽

2022 ◽

Vol 31 (1) ◽

pp. 1-50

Author(s):

Jianyi Zhou ◽

Junjie Chen ◽

Dan Hao

Keyword(s):

Extensive Study ◽

Good Choice ◽

Regression Testing ◽

Sequential Test ◽

Testing Time ◽

Parallel Test ◽

Parallel Testing ◽

Test Execution ◽

Coverage Criterion ◽

Test Prioritization

Although regression testing is important to guarantee the software quality in software evolution, it suffers from the widely known cost problem. To address this problem, existing researchers made dedicated efforts on test prioritization, which optimizes the execution order of tests to detect faults earlier; while practitioners in industry leveraged more computing resources to save the time cost of regression testing. By combining these two orthogonal solutions, in this article, we define the problem of parallel test prioritization, which is to conduct test prioritization in the scenario of parallel test execution to reduce the cost of regression testing. Different from traditional sequential test prioritization, parallel test prioritization aims at generating a set of test sequences, each of which is allocated in an individual computing resource and executed in parallel. In particular, we propose eight parallel test prioritization techniques by adapting the existing four sequential test prioritization techniques, by including and excluding testing time in prioritization. To investigate the performance of the eight parallel test prioritization techniques, we conducted an extensive study on 54 open-source projects and a case study on 16 commercial projects from Baidu , a famous search service provider with 600M monthly active users. According to the two studies, parallel test prioritization does improve the efficiency of regression testing, and cost-aware additional parallel test prioritization technique significantly outperforms the other techniques, indicating that this technique is a good choice for practical parallel testing. Besides, we also investigated the influence of two external factors, the number of computing resources and time allowed for parallel testing, and find that more computing resources indeed improve the performance of parallel test prioritization. In addition, we investigated the influence of two more factors, test granularity and coverage criterion, and find that parallel test prioritization can still accelerate regression testing in parallel scenario. Moreover, we investigated the benefit of parallel test prioritization on the regression testing process of continuous integration, considering both the cumulative acceleration performance and the overhead of prioritization techniques, and the results demonstrate the superiority of parallel test prioritization.