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Prioritization of Regression Tests using Singular Value Decomposition with Empirical Change Records

Mark Sherriff, Mike Lake, Laurie Williams
2007 The 18th IEEE International Symposium on Software Reliability (ISSRE '07)  
We propose a methodology for determining the effect of a change and then prioritizing regression test cases by gathering software change records and analyzing them through singular value decomposition.  ...  We performed a post hoc case study using this technique with three minor releases of a software product at IBM.  ...  Summary In this paper, we explored an empirically-based regression test prioritization method based upon structures discovered through change records and singular value decomposition.  ... 
doi:10.1109/issre.2007.25 dblp:conf/issre/SherriffLW07 fatcat:zucqp2mkuzgzvox5mro4aw4nue

Change Impact Analysis: A Tool for Effective Regression Testing [chapter]

Prem Parashar, Rajesh Bhatia, Arvind Kalia
2011 Communications in Computer and Information Science  
Different researchers have proposed different change impact analysis approaches that help in prioritization of test cases for regression testing.  ...  The results of the proposed algorithm showed that the importance of a module is an essential attribute in forming a prioritized test suite for regression testing.  ...  Sherriff [17, 18] et al. proposed a change impact analysis approach based upon singular value decomposition (SVD) to find the structure of the file association clusters and the amount of variation done  ... 
doi:10.1007/978-3-642-19423-8_17 fatcat:7xpprn4h4vdnrkvypn66rabceu

Empirical Software Change Impact Analysis using Singular Value Decomposition

Mark Sherriff, Laurie Williams
2008 2008 International Conference on Software Testing, Verification, and Validation  
We propose a methodology for determining the impact of a new system modification by analyzing software change records through singular value decomposition.  ...  Change records created from verification and validation efforts show how files in the system tend to change together in response to fixes for identified faults and failures.  ...  We have also applied our SVD methodology to regression test prioritization [20] .  ... 
doi:10.1109/icst.2008.25 dblp:conf/icst/SherriffW08 fatcat:qcs4gm3l65eblnan6dqhjoqzcm

An Approach for Test Case Prioritization Based on Three Factors

Manika Tyagi, Sona Malhotra
2015 International Journal of Information Technology and Computer Science  
The proposed approach is compared with different prioritization techniques such as no prioritization, reverse prioritization, random prioritization, and also with previous work of kavitha et al [6], using  ...  Therefore, it is necessary to discover the techniques with the goal of increasing the regression testing's effectiveness, by arranging test cases of test suites according to some objective criteria.  ...  value decomposition.  ... 
doi:10.5815/ijitcs.2015.04.09 fatcat:6yl2qrqewfbehbmms5uallo64i

Prioritizing system-reliability prediction improvements

D.W. Coit, T. Jin
2001 IEEE Transactions on Reliability  
System-reliability predictions often use data and models from a variety of sources, each with differing degrees of estimation uncertainty.  ...  RPPI is based on a decomposition of the variance of the systemreliability or on a mean-time-to-failure estimate.  ...  His measure characterizes the rate at which the system-reliability changes with respect to changes in the reliability of a given component.  ... 
doi:10.1109/24.935012 fatcat:x6yfeajaojfijg3mg3obv2u5pi

Protein co-expression network analysis (ProCoNA)

David L Gibbs, Arie Baratt, Ralph S Baric, Yoshihiro Kawaoka, Richard D Smith, Eric S Orwoll, Michael G Katze, Shannon K McWeeney
2013 Journal of Clinical Bioinformatics  
Results: We have evaluated the construction of network models using methods derived from weighted gene co-expression network analysis (WGCNA).  ...  The module representatives, called eigenpeptides, correlate significantly with biological phenotypes.  ...  The first right-singular vector, or eigenpeptide, is computed from a singular value decomposition of the standardized abundance module matrix.  ... 
doi:10.1186/2043-9113-3-11 pmid:23724967 pmcid:PMC3695838 fatcat:w7p6sqijdzhgtdbrd77wrs2hba

An Empirical Study on the Use of Defect Prediction for Test Case Prioritization

David Paterson, Jose Campos, Rui Abreu, Gregory M. Kapfhammer, Gordon Fraser, Phil McMinn
2019 2019 12th IEEE Conference on Software Testing, Validation and Verification (ICST)  
The experiments reveal that using defect prediction to prioritize test cases reduces the number of test cases required to find a fault by on average 9.48% when compared with existing coverage-based strategies  ...  Using 6 real-world Java programs containing 395 real faults, we conducted an empirical evaluation comparing this paper's strategy, called G-clef, against eight existing test case prioritization strategies  ...  ACKNOWLEDGMENTS This work was partially funded by the ERDF through COMPETE 2020 Program and by National Funds through the Portuguese funding agency (FCT) with reference UID/CEC/50021/2019, and by the FaultLocker  ... 
doi:10.1109/icst.2019.00041 dblp:conf/icst/PatersonCAKFM19 fatcat:a2l2loyr3fdqnh5vlkdh7yozty

ComReg: A Complex Network Approach to Prioritize Test Cases for Regression Testing [article]

Imrul Kayes, Jacob Chakareski
2014 arXiv   pre-print
We also discuss the use of fault communities to select an arbitrary percentage of the test cases from a prioritized regression test suite.  ...  Regression testing is performed to provide confidence that changes in a part of software do not affect other parts of the software.  ...  They proposed a methodology for determining the effect of a software feature change and then prioritized regression test cases by gathering software change records and analyzing them through singular value  ... 
arXiv:1311.4176v3 fatcat:lozjyx76zremxj776dvr74mmue

Hybrid Models Based on Singular Values and Autoregressive Methods for Multistep Ahead Forecasting of Traffic Accidents

Lida Barba, Nibaldo Rodríguez
2016 Mathematical Problems in Engineering  
Additionally, equivalent models that combine Hankel Singular Value Decomposition (HSVD), AR, and ANN are evaluated.  ...  A simplified form of Singular Spectrum Analysis (SSA), combined with the autoregressive linear (AR) method, and a conventional Artificial Neural Network (ANN) are proposed.  ...  Competing Interests The authors declare that they have no competing interests regarding the publication of this paper.  ... 
doi:10.1155/2016/2030647 fatcat:kiuazr7ijzh4ljprqnamlwdbee

Phiclust: a clusterability measure for single-cell transcriptomics reveals phenotypic subpopulations

Maria Mircea, Mazène Hochane, Xueying Fan, Susana M. Chuva de Sousa Lopes, Diego Garlaschelli, Stefan Semrau
2022 Genome Biology  
Here, we present phiclust (ϕclust), a clusterability measure derived from random matrix theory that can be used to identify cell clusters with non-random substructure, testably leading to the discovery  ...  of previously overlooked phenotypes.  ...  Acknowledgements We thank Ahmed Mahfouz for valuable feedback on the manuscript and the staff of Gynaikon, Rotterdam, as well as the anonymous tissue donors for the human fetal material.  ... 
doi:10.1186/s13059-021-02590-x pmid:35012604 pmcid:PMC8751334 fatcat:te636xh65fdmnmi74k22u6q3pm

Feature-space selection with banded ridge regression [article]

Tom Dupre la Tour, Michael Eickenberg, Jack L Gallant
2022 bioRxiv   pre-print
Banded ridge regression is then mathematically linked to a number of other regression methods with similar feature-space selection mechanisms.  ...  In this framework, a stimulus representation is expressed as a feature space and is used in a regularized linear regression to predict brain activity.  ...  Institutes of Health (B-U01EB02), the Weill Neurohub at University of California, Berkeley, and internal funds from University of California, Berkeley.  ... 
doi:10.1101/2022.05.05.490831 fatcat:vxkot3ohrjbyjesbbumo5sytiq

Using SNP Weights Derived From Gene Expression Modules to Improve GWAS Power for Feed Efficiency in Pigs

Brittney N. Keel, Warren M. Snelling, Amanda K. Lindholm-Perry, William T. Oliver, Larry A. Kuehn, Gary A. Rohrer
2020 Frontiers in Genetics  
was performed, using constrained tensor decomposition, to obtain a total of 10 gene expression modules.  ...  testing to conduct a GWAS.  ...  There was a common pattern to the change in heritability estimates as the SNP prioritization changed.  ... 
doi:10.3389/fgene.2019.01339 pmid:32038708 pmcid:PMC6985563 fatcat:xn5dkuvvjbg7zoe24n5aixagdi

Three-way clustering of multi-tissue multi-individual gene expression data using constrained tensor decomposition [article]

Miaoyan Wang, Jonathan Fischer, Yun S. Song
2017 bioRxiv   pre-print
Through simulation and application to the GTEx RNA-seq data, we show that our tensor decomposition identifies three-way clusters with higher accuracy, while being 11x faster, than the competing Bayesian  ...  To this end, we propose a new method, called MultiCluster, based on constrained tensor decomposition which permits the investigation of transcriptome variation across individuals and tissues simultaneously  ...  This research is supported in part by a Math+X Research Grant from the Simons Foundation, a Packard Fellowship for Science and Engineering, and a National Institutes of Health grant R01-GM094402.  ... 
doi:10.1101/229245 fatcat:pgw4hmiebrbmzediyaxuynefdy

Towards Bursting Filter Bubble via Contextual Risks and Uncertainties [article]

Rikiya Takahashi, Shunan Zhang
2017 arXiv   pre-print
The posterior of the contextual coefficients can be computed efficiently using a low-rank version of Laplace's method via thin Singular Value Decomposition.  ...  with lack of serendipity.  ...  (2) , one can find that each matrix V (·) is obtained by thin Singular Value Decomposition (SVD).  ... 
arXiv:1706.09985v1 fatcat:nsullixgerdzpou42zfze4m6me

Polygenic Risk Score in African populations: progress and challenges

Yagoub Adam, Suraju Sadeeq, Judit Kumuthini, Olabode Ajayi, Gordon Wells, Rotimi Solomon, Olubanke Ogunlana, Emmanuel Adetiba, Emeka Iweala, Benedikt Brors, Ezekiel Adebiyi
2022 F1000Research  
PRS analysis addresses the shortfall of GWAS by taking into account the SNPs/alleles with low effect size but play an indispensable role to the observed phenotypic/trait variance.  ...  Moreover, it selects single nucleotide polymorphisms (SNPs) that contribute to the disease with low effect size making it more precise at individual level risk prediction.  ...  Acknowledgements Authors acknowledge the logistical support of Mr. Babajide Ayodele. Covenant University provided the infrastructural support.  ... 
doi:10.12688/f1000research.76218.1 fatcat:5uhdrcxdpjg2nernjaklxbqk7q
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