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Journal articles on the topic 'Software regression'

Author: Grafiati

Published: 4 June 2021

Last updated: 17 February 2022

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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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Muccini, Henry, Marcio S. Dias, and Debra J. Richardson. "Towards software architecture-based regression testing." ACM SIGSOFT Software Engineering Notes 30, no. 4 (2005): 1–7. http://dx.doi.org/10.1145/1082983.1083223.

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12

Rothermel, Gregg, Mary Jean Harrold, and Jeinay Dedhia. "Regression test selection for C++ software." Software Testing, Verification and Reliability 10, no. 2 (2000): 77–109. http://dx.doi.org/10.1002/1099-1689(200006)10:2<77::aid-stvr197>3.0.co;2-e.

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13

Blackwell, J. Lloyd. "The reluctant confessions of regression software." Atlantic Economic Journal 27, no. 3 (1999): 353. http://dx.doi.org/10.1007/bf02299586.

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14

Gulezian, Ronald. "Handling regression subsets in software modeling." Journal of Systems and Software 33, no. 1 (1996): 81–86. http://dx.doi.org/10.1016/0164-1212(95)00122-0.

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15

Lee, Mi-Jin, and Eun-Man Choi. "Regression Testing of Software Evolution by AOP." KIPS Transactions:PartD 15D, no. 4 (2008): 495–504. http://dx.doi.org/10.3745/kipstd.2008.15-d.4.495.

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16

Ohyver, Margaretha. "Pemodelan Principal Component Regression dengan Software R." ComTech: Computer, Mathematics and Engineering Applications 3, no. 1 (2012): 177. http://dx.doi.org/10.21512/comtech.v3i1.2400.

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Principal Component Regression (PCR) is one method to handle multicollinear problems. PCR produces principal components that have a VIF less than ten. The purpose for this research is to obtained PCR model using R software. The result is a model of PCR with two principal components and determination coefficients R(square) = 97,27%.

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17

Lou, Jungang, Yunliang Jiang, Qing Shen, Zhangguo Shen, Zhen Wang, and Ruiqin Wang. "Software reliability prediction via relevance vector regression." Neurocomputing 186 (April 2016): 66–73. http://dx.doi.org/10.1016/j.neucom.2015.12.077.

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18

Memon, Atif, Adithya Nagarajan, and Qing Xie. "Automating regression testing for evolving GUI software." Journal of Software Maintenance and Evolution: Research and Practice 17, no. 1 (2005): 27–64. http://dx.doi.org/10.1002/smr.305.

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19

Mansour, Nashat, Husam Takkoush, and Ali Nehme. "UML-based regression testing for OO software." Journal of Software Maintenance and Evolution: Research and Practice 23, no. 1 (2011): 51–68. http://dx.doi.org/10.1002/smr.508.

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20

Orso, Alessandro, Nanjuan Shi, and Mary Jean Harrold. "Scaling regression testing to large software systems." ACM SIGSOFT Software Engineering Notes 29, no. 6 (2004): 241–51. http://dx.doi.org/10.1145/1041685.1029928.

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21

HAWORTH, DWIGHT A. "Regression Control Charts to Manage Software Maintenance." Journal of Software Maintenance: Research and Practice 8, no. 1 (1996): 35–48. http://dx.doi.org/10.1002/(sici)1096-908x(199601)8:1<35::aid-smr124>3.0.co;2-#.

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22

Salem, Ahmed M., Kamel Rekab, and James A. Whittaker. "Prediction of software failures through logistic regression." Information and Software Technology 46, no. 12 (2004): 781–89. http://dx.doi.org/10.1016/j.infsof.2003.10.008.

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23

Krautkramer, Wells. "Multidimensional regression software for production line testing." NDT International 23, no. 3 (1990): 180. http://dx.doi.org/10.1016/0308-9126(90)90252-j.

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24

Munson, J. C., and T. M. Khoshgoftaar. "Regression modelling of software quality: empirical investigation." Information and Software Technology 32, no. 2 (1990): 106–14. http://dx.doi.org/10.1016/0950-5849(90)90109-5.

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25

Miyazaki, Y., M. Terakado, K. Ozaki, and H. Nozaki. "Robust regression for developing software estimation models." Journal of Systems and Software 27, no. 1 (1994): 3–16. http://dx.doi.org/10.1016/0164-1212(94)90110-4.

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26

Xizi, Huang. "Non-linear regression for predicting software reliability." Microelectronics Reliability 28, no. 6 (1988): 865–66. http://dx.doi.org/10.1016/0026-2714(88)90283-1.

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27

Yücalar, Fatih, Deniz Kilinc, Emin Borandag, and Akin Ozcift. "Regression Analysis Based Software Effort Estimation Method." International Journal of Software Engineering and Knowledge Engineering 26, no. 05 (2016): 807–26. http://dx.doi.org/10.1142/s0218194016500261.

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Estimating the development effort of a software project in the early stages of the software life cycle is a significant task. Accurate estimates help project managers to overcome the problems regarding budget and time overruns. This paper proposes a new multiple linear regression analysis based effort estimation method, which has brought a different perspective to the software effort estimation methods and increased the success of software effort estimation processes. The proposed method is compared with standard Use Case Point (UCP) method, which is a well-known method in this area, and simpl

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28

Wheeler, Bob. "Regression Tool Kit: Software for the Design and Analysis of Regression Experiments." American Statistician 39, no. 2 (1985): 144. http://dx.doi.org/10.2307/2682823.

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29

Muruzábal, Jorge, Diego Vidaurre, and Julián Sánchez. "SOMwise regression: a new clusterwise regression method." Neural Computing and Applications 21, no. 6 (2011): 1229–41. http://dx.doi.org/10.1007/s00521-011-0536-3.

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30

Chatzipetrou, Panagiota. "Software Cost Estimation." International Journal of Service Science, Management, Engineering, and Technology 10, no. 3 (2019): 14–31. http://dx.doi.org/10.4018/ijssmet.2019070102.

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Software cost estimation (SCE) is a critical phase in software development projects. A common problem in building software cost models is that the available datasets contain projects with lots of missing categorical data. There are several techniques for handling missing data in the context of SCE. The purpose of this article is to show a state-of-art statistical and visualization approach of evaluating and comparing the effect of missing data on the accuracy of cost estimation models. Five missing data techniques were used: multinomial logistic regression, listwise deletion, mean imputation,

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31

Shull, Forrest. "Progression, Regression, or Stasis?" IEEE Software 31, no. 1 (2014): 4–8. http://dx.doi.org/10.1109/ms.2014.11.

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32

Zamostny, Petr, and Zdenek Belohlav. "A software for regression analysis of kinetic data." Computers & Chemistry 23, no. 5 (1999): 479–85. http://dx.doi.org/10.1016/s0097-8485(99)00024-8.

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33

Nassif, Ali Bou, Mohammad Azzeh, Ali Idri, and Alain Abran. "Software Development Effort Estimation Using Regression Fuzzy Models." Computational Intelligence and Neuroscience 2019 (February 20, 2019): 1–17. http://dx.doi.org/10.1155/2019/8367214.

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Software effort estimation plays a critical role in project management. Erroneous results may lead to overestimating or underestimating effort, which can have catastrophic consequences on project resources. Machine-learning techniques are increasingly popular in the field. Fuzzy logic models, in particular, are widely used to deal with imprecise and inaccurate data. The main goal of this research was to design and compare three different fuzzy logic models for predicting software estimation effort: Mamdani, Sugeno with constant output, and Sugeno with linear output. To assist in the design of

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34

Mahdian, Alireza, Anneliese Amschler Andrews, and Orest Jacob Pilskalns. "Regression testing with UML software designs: A survey." Journal of Software Maintenance and Evolution: Research and Practice 21, no. 4 (2009): 253–86. http://dx.doi.org/10.1002/smr.403.

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35

Biswas, Swarnendu, Rajib Mall, and Manoranjan Satpathy. "A regression test selection technique for embedded software." ACM Transactions on Embedded Computing Systems 13, no. 3 (2013): 1–39. http://dx.doi.org/10.1145/2539036.2539043.

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36

García-Floriano, Andrés, Cuauhtémoc López-Martín, Cornelio Yáñez-Márquez, and Alain Abran. "Support vector regression for predicting software enhancement effort." Information and Software Technology 97 (May 2018): 99–109. http://dx.doi.org/10.1016/j.infsof.2018.01.003.

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37

Sentas, Panagiotis, Lefteris Angelis, Ioannis Stamelos, and George Bleris. "Software productivity and effort prediction with ordinal regression." Information and Software Technology 47, no. 1 (2005): 17–29. http://dx.doi.org/10.1016/j.infsof.2004.05.001.

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38

Nouri, Ayoub, Peter Poplavko, Lefteris Angelis, Alexandros Zerzelidis, Saddek Bensalem, and Panagiotis Katsaros. "Maximal software execution time: a regression-based approach." Innovations in Systems and Software Engineering 14, no. 2 (2018): 101–16. http://dx.doi.org/10.1007/s11334-018-0314-9.

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39

Bibi, S., G. Tsoumakas, I. Stamelos, and I. Vlahavas. "Regression via Classification applied on software defect estimation." Expert Systems with Applications 34, no. 3 (2008): 2091–101. http://dx.doi.org/10.1016/j.eswa.2007.02.012.

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40

Fisal, Nagwa R., Abeer Hamdy, and Essam A. Rashed. "Search-Based Regression Testing Optimization." International Journal of Open Source Software and Processes 12, no. 2 (2021): 1–20. http://dx.doi.org/10.4018/ijossp.2021040101.

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Regression testing is one of the essential activities during the maintenance phase of software projects. It is executed to ensure the validity of an altered software. However, as the software evolves, regression testing becomes prohibitively expensive. In order to reduce the cost of regression testing, it is mandatory to reduce the size of the test suite by selecting the most representative test cases that do not compromise the effectiveness of the regression testing in terms of fault-detection capability. This problem is known as test suite reduction (TSR) problem, and it is known to be an NP

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41

Zeng, Wenyi, Qilei Feng, and Junhong Li. "Fuzzy least absolute linear regression." Applied Soft Computing 52 (March 2017): 1009–19. http://dx.doi.org/10.1016/j.asoc.2016.09.029.

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42

CHEN, Shu-feng, and Hong-yuan ZHENG. "Dependence analysis and regression testing of object-oriented software." Journal of Computer Applications 29, no. 11 (2009): 3110–13. http://dx.doi.org/10.3724/sp.j.1087.2009.03110.

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43

Marza, Venus, and Mir Ali Seyyedi. "Fuzzy Multiple Regression Model for Estimating Software Development Time." International Journal of Engineering Business Management 1 (March 2009): 9. http://dx.doi.org/10.5772/6775.

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44

Гржибовский, А. М., Т. Н. Унгуряну, М. А. Горбатова, and Н. В. Саввина. "Multiple linear regression analysis using SPSS and STATA software." Научно-практический журнал «Наркология», no. 1() (March 5, 2018): 19–31. http://dx.doi.org/10.25557/1682-8313.2018.01.19-31.

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Рассматриваются основные принципы применения множественного линейного регрессионного анализа для ситуаций с одной зависимой и несколькими независимыми переменными с использованием пакетов статистических программ SPSS и STATA. Материал дает общие представления о множественном линейном регрессионном анализе и не заменяет изучения специализированной литературы. Особое внимание уделяется проверке соблюдения необходимых условий для применения множественного линейного регрессионного анализа и интерпретации его результатов. Даются рекомендации о том, как следует представлять результаты множественного

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45

Гржибовский, А. М., Т. Н. Унгуряну, М. А. Горбатова, and Н. В. Саввина. "Multiple linear regression analysis using SPSS and STATA software." Научно-практический журнал «Наркология», no. 1() (February 28, 2018): 19–31. http://dx.doi.org/10.25557/igpp.2018.1.10756.

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Рассматриваются основные принципы применения множественного линейного регрессионного анализа для ситуаций с одной зависимой и несколькими независимыми переменными с использованием пакетов статистических программ SPSS и STATA. Материал дает общие представления о множественном линейном регрессионном анализе и не заменяет изучения специализированной литературы. Особое внимание уделяется проверке соблюдения необходимых условий для применения множественного линейного регрессионного анализа и интерпретации его результатов. Даются рекомендации о том, как следует представлять результаты множественного

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46

Sandamali Dharmasena, L., and P. Zeephongsekul. "Fitting software reliability growth curves using nonparametric regression methods." Statistical Methodology 7, no. 2 (2010): 109–20. http://dx.doi.org/10.1016/j.stamet.2009.10.007.

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47

Gunst, Richard F. "Regression and ANOVA: An Integrated Approach Using SAS Software." Technometrics 45, no. 2 (2003): 170–71. http://dx.doi.org/10.1198/tech.2003.s159.

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48

Oliveira, Adriano L. I. "Estimation of software project effort with support vector regression." Neurocomputing 69, no. 13-15 (2006): 1749–53. http://dx.doi.org/10.1016/j.neucom.2005.12.119.

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49

Boggs, Paul T., Janet R. Donaldson, Richaard h. Byrd, and Robert B. Schnabel. "Algorithm 676: ODRPACK: software for weighted orthogonal distance regression." ACM Transactions on Mathematical Software 15, no. 4 (1989): 348–64. http://dx.doi.org/10.1145/76909.76913.

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50

Chatzis, Sotirios P., and Andreas S. Andreou. "Maximum Entropy Discrimination Poisson Regression for Software Reliability Modeling." IEEE Transactions on Neural Networks and Learning Systems 26, no. 11 (2015): 2689–701. http://dx.doi.org/10.1109/tnnls.2015.2391171.

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