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Relevant bibliographies by topics / Software regression

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Academic literature on the topic 'Software regression'

Author: Grafiati

Published: 4 June 2021

Last updated: 17 February 2022

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Journal articles on the topic "Software regression"

1

Muccini, Henry, Marcio Dias, and Debra J. Richardson. "Software architecture-based regression testing." Journal of Systems and Software 79, no. 10 (2006): 1379–96. http://dx.doi.org/10.1016/j.jss.2006.02.059.

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Sullivan, Sheena G., and Sander Greenland. "Bayesian regression in SAS software." International Journal of Epidemiology 42, no. 1 (2012): 308–17. http://dx.doi.org/10.1093/ije/dys213.

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Sullivan, S., and S. Greenland. "Bayesian regression in SAS software." International Journal of Epidemiology 43, no. 5 (2014): 1667–68. http://dx.doi.org/10.1093/ije/dyu189.

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Sokal, Robert R. "LogXact: Logistic Regression Software Featuring Exact Methods.LogXact-Turbo: Logistic Regression Software Featuring Exact Methods." Quarterly Review of Biology 70, no. 1 (1995): 127. http://dx.doi.org/10.1086/418974.

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Armah, Gabriel Kofi, Guanchun Luo, Ke Qin, and Angolo Shem Mbandu. "Applying Variant Variable Regularized Logistic Regression for Modeling Software Defect Predictor." Lecture Notes on Software Engineering 4, no. 2 (2016): 107–15. http://dx.doi.org/10.7763/lnse.2016.v4.234.

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Taylor, Greg. "Credibility, Hypothesis Testing and Regression Software." ASTIN Bulletin 37, no. 02 (2007): 517–35. http://dx.doi.org/10.2143/ast.37.2.2024078.

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It has been known since Zehnwirth (1977) that a scalar credibility coefficient is closely related to the F-statistic of an analysis of variance between and within risk clauses. The F-statistic may also be viewed as testing a certain regression structure, associated with credibility framework, against the null hypothesis of a simpler structure. This viewpoint is extended to multi-dimensional credibility frameworks in which the credibility coefficient is a matrix (Sections 3 and 4), and to hierarchical regression credibility frameworks (Section 6). In each case the credibility coefficient is exp

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Taylor, Greg. "Credibility, Hypothesis Testing and Regression Software." ASTIN Bulletin 37, no. 2 (2007): 517–35. http://dx.doi.org/10.1017/s0515036100014975.

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It has been known since Zehnwirth (1977) that a scalar credibility coefficient is closely related to the F-statistic of an analysis of variance between and within risk clauses. The F-statistic may also be viewed as testing a certain regression structure, associated with credibility framework, against the null hypothesis of a simpler structure.This viewpoint is extended to multi-dimensional credibility frameworks in which the credibility coefficient is a matrix (Sections 3 and 4), and to hierarchical regression credibility frameworks (Section 6). In each case the credibility coefficient is expr

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Calonico, Sebastian, Matias D. Cattaneo, Max H. Farrell, and Rocío Titiunik. "Rdrobust: Software for Regression-discontinuity Designs." Stata Journal: Promoting communications on statistics and Stata 17, no. 2 (2017): 372–404. http://dx.doi.org/10.1177/1536867x1701700208.

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We describe a major upgrade to the Stata (and R) rdrobust package, which provides a wide array of estimation, inference, and falsification methods for the analysis and interpretation of regression-discontinuity designs. The main new features of this upgraded version are as follows: i) covariate-adjusted bandwidth selection, point estimation, and robust bias-corrected inference, ii) cluster–robust bandwidth selection, point estimation, and robust bias-corrected inference, iii) weighted global polynomial fits and pointwise confidence bands in regression-discontinuity plots, and iv) several new b

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Harrold, Mary Jean, James A. Jones, Tongyu Li, et al. "Regression test selection for Java software." ACM SIGPLAN Notices 36, no. 11 (2001): 312–26. http://dx.doi.org/10.1145/504311.504305.

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KHOSHGOFTAAR, TAGHI M., and EDWARD B. ALLEN. "LOGISTIC REGRESSION MODELING OF SOFTWARE QUALITY." International Journal of Reliability, Quality and Safety Engineering 06, no. 04 (1999): 303–17. http://dx.doi.org/10.1142/s0218539399000292.

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Reliable software is mandatory for complex mission-critical systems. Classifying modules as fault-prone, or not, is a valuable technique for guiding development processes, so that resources can be focused on those parts of a system that are most likely to have faults. Logistic regression offers advantages over other classification modeling techniques, such as interpretable coefficients. There are few prior applications of logistic regression to software quality models in the literature, and none that we know of account for prior probabilities and costs of misclassification. A contribution of t

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Dissertations / Theses on the topic "Software regression"

1

Mostafa, Abdelelah M. "Regression approach to software reliability models." [Tampa, Fla] : University of South Florida, 2006. http://purl.fcla.edu/usf/dc/et/SFE0001648.

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Arafeen, Md Junaid. "Adaptive Regression Testing Strategy: An Empirical Study." Thesis, North Dakota State University, 2012. https://hdl.handle.net/10365/26525.

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When software systems evolve, different amounts of code modifications can be involved in different versions. These factors can affect the costs and benefits of regression testing techniques, and thus, there may be no single regression testing technique that is the most cost-effective technique to use on every version. To date, many regression testing techniques have been proposed, but no research has been done on the problem of helping practitioners systematically choose appropriate techniques on new versions as systems evolve. To address this problem, we propose adaptive regression testing (A

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Silveira, Neto Paulo Anselmo da Mota. "A regression testing approach for software product lines architectures." Universidade Federal de Pernambuco, 2010. https://repositorio.ufpe.br/handle/123456789/2346.

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Made available in DSpace on 2014-06-12T15:57:06Z (GMT). No. of bitstreams: 2 arquivo3140_1.pdf: 4172532 bytes, checksum: b7cc6b23a678d29aa1a5c9c9da0617e9 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2010<br>Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco<br>Com o objetivo de produzir produtos individualizados, muitas vezes, as empresas se deparam com a necessidade de altos investimentos, elevando assim os preços de produtos individualizados. A partir dessa necessidade, muitas empresas, começaram a introduzir o co

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Richmond, James Howard. "Bayesian Logistic Regression Models for Software Fault Localization." Case Western Reserve University School of Graduate Studies / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=case1326658577.

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Mo, Lijia. "Examining the reliability of logistic regression estimation software." Diss., Kansas State University, 2010. http://hdl.handle.net/2097/7059.

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Doctor of Philosophy<br>Department of Agricultural Economics<br>Allen M. Featherstone<br>Bryan W. Schurle<br>The reliability of nine software packages using the maximum likelihood estimator for the logistic regression model were examined using generated benchmark datasets and models. Software packages tested included: SAS (Procs Logistic, Catmod, Genmod, Surveylogistic, Glimmix, and Qlim), Limdep (Logit, Blogit), Stata (Logit, GLM, Binreg), Matlab, Shazam, R, Minitab, Eviews, and SPSS for all available algorithms, none of which have been previously tested. This study expands on the existing

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Duncombe, P. "Statistical software and an example of a regression package." Thesis, University of Kent, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.234478.

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Minhas, Nasir Mehmood. "Regression Testing Challenges and Solutions : An Industry-Academia Perspective." Licentiate thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-17381.

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Background: Software quality assurance (QA) is an essential activity in the software development lifecycle. Among the different QA activities, regression testing is a challenging task for large-scale software development. Regression testing is a well-researched area, and a large number of techniques have been proposed to fulfill the needs of industry. Despite the extensive research, the adoption of proposed regression testing techniques in the industry is limited. Studies show that there is a visible gap between research and practice. Objective: This work aims at reducing the gap between indus

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Mehta, Alok. "Evolving legacy system's features into fine-grained components using regression test-cases." Link to electronic thesis, 2002. http://www.wpi.edu/Pubs/ETD/Available/etd-1211102-163800.

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Dissertatio (Ph. D.)--Worcester Polytechnic Institute.<br>Keywords: software maintenance; software evolution; regression test-cases; components; legacy system; incremental software evolution methodology; fine-grained components. Includes bibliographical references (p. 283-294).

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Alshathry, Omar. "A regression-based model for optimising cost of software quality assurance." Thesis, De Montfort University, 2010. http://hdl.handle.net/2086/4536.

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Kuchimanchi, Sriram. "Towards a regression test selection technique for message-based software integration." ScholarWorks@UNO, 2004. http://louisdl.louislibraries.org/u?/NOD,164.

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Thesis (M.S.)--University of New Orleans, 2004.<br>Title from electronic submission form. "A thesis ... in partial fulfillment of the requirements for the degree of Master of Science in the Department of Computer Science."--Thesis t.p. Vita. Includes bibliographical references.

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Books on the topic "Software regression"

1

Rothermel, Gregg. Regression test selection for C++ software. Oregon State University, Dept. of Computer Science, 1999.

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C, Littell Ramon, and SAS Institute, eds. SAS System for regression. 2nd ed. SAS Institute, 1991.

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C, Littell Ramon, ed. SAS System for regression. 3rd ed. SAS Institute, 2000.

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Elbaum, Sebastian. Prioritizing test cases for regression testing. Oregon State University, Dept. of Computer Science, 2000.

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Bible, John. A unifying framework supporting the analysis and development of safe regression test selection techniques. Oregon State University, Dept. of Computer Science, 1999.

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N, Naik Dayanand, ed. Applied multivariate statistics with SAS software. 2nd ed. SAS Institute, 1999.

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N, Naik Dayanand, ed. Applied multivariate statistics with SAS software. SAS Institute, 1995.

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Khattree, Ravindra. Applied multivariate statistics with SAS software. 2nd ed. SAS Institute, 1999.

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Asquith, William H. Site-specific estimation of peak-streamflow frequency using generalized least-squares regression for natural basins in Texas. U.S. Dept. of the Interior, U.S. Geological Survey, 1999.

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Asquith, William H. Site-specific estimation of peak-streamflow frequency using generalized least-squares regression for natural basins in Texas. U.S. Dept. of the Interior, U.S. Geological Survey, 1999.

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Book chapters on the topic "Software regression"

1

Harrell, Frank E. "R Software." In Regression Modeling Strategies. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19425-7_6.

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Harrell, Frank E. "S-Plus Software." In Regression Modeling Strategies. Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3462-1_6.

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Green, Peter J., and Bernard W. Silverman. "Available software." In Nonparametric Regression and Generalized Linear Models. Springer US, 1994. http://dx.doi.org/10.1007/978-1-4899-4473-3_8.

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Trendowicz, Adam, and Ross Jeffery. "Statistical Regression Analysis." In Software Project Effort Estimation. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03629-8_8.

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Backes, John, Suzette Person, Neha Rungta, and Oksana Tkachuk. "Regression Verification Using Impact Summaries." In Model Checking Software. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39176-7_7.

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Trendowicz, Adam, and Ross Jeffery. "Classification and Regression Trees." In Software Project Effort Estimation. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03629-8_10.

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Ruczinski, Ingo, Charles Kooperberg, and Michael LeBlanc. "Logic Regression — Methods and Software." In Nonlinear Estimation and Classification. Springer New York, 2003. http://dx.doi.org/10.1007/978-0-387-21579-2_21.

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Cai, Kai-Yuan. "Empirical Regression Methods." In The Kluwer International Series in Software Engineering. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5593-3_2.

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Batra, Gagandeep, Yogesh Kumar Arora, and Jyotsna Sengupta. "Model-Based Software Regression Testing for Software Components." In Information Systems, Technology and Management. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00405-6_18.

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Bian, Yi, Serkan Kirbas, Mark Harman, Yue Jia, and Zheng Li. "Regression Test Case Prioritisation for Guava." In Search-Based Software Engineering. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22183-0_15.

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Conference papers on the topic "Software regression"

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Hartmann, J., and D. J. Robson. "Approaches to regression testing." In 1988 Conference on Software Maintenance. IEEE, 1988. http://dx.doi.org/10.1109/icsm.1988.10189.

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Ngah, Amir, Malcolm Munro, and Keith Gallagher. "Regression Test Selection Model using Decomposition Slicing." In Software Engineering. ACTAPRESS, 2012. http://dx.doi.org/10.2316/p.2012.780-020.

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von Mayrhauser, Mraz, and Walls. "Domain based regression testing." In Proceedings International Conference on Software Maintenance. IEEE Comput. Soc. Press, 1994. http://dx.doi.org/10.1109/icsm.1994.336792.

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Guowei Yang, M. B. Dwyer, and G. Rothermel. "Regression model checking." In 2009 IEEE International Conference on Software Maintenance (ICSM 2009). IEEE, 2009. http://dx.doi.org/10.1109/icsm.2009.5306334.

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Harrold, Mary Jean, James A. Jones, Tongyu Li, Donglin Liang, and Ashish Gujarathi. "Regression test selection for Java software." In the 16th ACM SIGPLAN conference. ACM Press, 2001. http://dx.doi.org/10.1145/504282.504305.

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Xu, Guoqing, and Atanas Rountev. "Regression Test Selection for AspectJ Software." In 29th International Conference on Software Engineering. IEEE, 2007. http://dx.doi.org/10.1109/icse.2007.72.

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Muccini, Henry, Marcio S. Dias, and Debra J. Richardson. "Towards software architecture-based regression testing." In the 2005 workshop. ACM Press, 2005. http://dx.doi.org/10.1145/1083217.1083223.

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Gomes Soares, Yanne Micaele, and Roberta Andrade de A. Fagundes. "Software time estimation using regression methods." In 2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI). IEEE, 2017. http://dx.doi.org/10.1109/la-cci.2017.8285723.

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Felsing, Dennis, Sarah Grebing, Vladimir Klebanov, Philipp Rümmer, and Mattias Ulbrich. "Automating regression verification." In ASE '14: ACM/IEEE International Conference on Automated Software Engineering. ACM, 2014. http://dx.doi.org/10.1145/2642937.2642987.

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Swearngin, Amanda, Myra B. Cohen, Bonnie E. John, and Rachel K. E. Bellamy. "Human performance regression testing." In 2013 35th International Conference on Software Engineering (ICSE). IEEE, 2013. http://dx.doi.org/10.1109/icse.2013.6606561.

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Reports on the topic "Software regression"

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Strichman, Ofer. Software Regression Verification. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada594501.

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Hoover, Alex. Software Change and Regression Testing. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada405771.

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Eubanks, Lloyd Lee, Katherine Letizia McFadden, Richard Frank Haaker, Douglas M. Osborn, and Nathan E. Bixler. Software regression quality assurance for MACCS2 : version 2.5.0.0 through version 2.5.0.9. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1051710.

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Zywicz, Edward. Summary of Documentation for DYNA3D-ParaDyn's Software Quality Assurance Regression Test Problems. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1335773.

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Dzhangarov, A. I., and H. A. Akhmetova. Multivariate Regression Analysis Software violation of the law on the normal distribution of observations. Ljournal, 2019. http://dx.doi.org/10.18411/5789-1123-8756-71622.

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Irimata,, Katherine, Donald Malec, Bringham Bastian, and Merianne Rose Spencer. Using SAS/STAT to Understand the NCI Joinpoint Regression Software: Testing for a Zero Slope Using Rates of Drugs Overdose Deaths Involving Fentanyl, 2011–2016. National Center for Health Statistics, 2021. http://dx.doi.org/10.15620/cdc:105105.

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