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Journal articles on the topic 'Regression test'

Author: Grafiati

Published: 4 June 2021

Last updated: 18 June 2025

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1

De Maio, Fernando. "Regression Analysis and the Sociological Imagination." Teaching Statistics 36, no. 2 (2013): 52–57. http://dx.doi.org/10.1111/test.12019.

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2

Seth, Ashish. "Regression Test Case Optimization Using Ant Colony Optimization." Journal of Advanced Research in Dynamical and Control Systems 12, no. 3 (2020): 111–18. http://dx.doi.org/10.5373/jardcs/v12i3/20201172.

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3

Korel, Bogdan, and Ali M. Al-Yami. "Automated regression test generation." ACM SIGSOFT Software Engineering Notes 23, no. 2 (1998): 143–52. http://dx.doi.org/10.1145/271775.271803.

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4

Croucher, John S. "Using some forensic anthropology history to illustrate regression." Teaching Statistics 41, no. 2 (2018): 69–72. http://dx.doi.org/10.1111/test.12185.

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5

Ansari, Ahlam, Anam Khan, Alisha Khan, and Konain Mukadam. "Optimized Regression Test Using Test Case Prioritization." Procedia Computer Science 79 (2016): 152–60. http://dx.doi.org/10.1016/j.procs.2016.03.020.

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6

Ansari, Ahlam, Alisha Khan, Anam Khan, and Konain Mukadam. "Test Case Prioritization for Optimizing a Regression Test." International Journal in Foundations of Computer Science & Technology 6, no. 2 (2016): 23–36. http://dx.doi.org/10.5121/ijfcst.2016.6202.

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7

Ahlam, Ansari1 Alisha Khan2 Anam Khan3 and Konain Mukadam4. "TEST CASE PRIORITIZATION FOR OPTIMIZING A REGRESSION TEST." International Journal on Foundations of Computer Science & Technology (IJFCST) 6, no. 2 (2023): 14. https://doi.org/10.5281/zenodo.7712776.

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Regression testing makes sure that upgradation of software in terms of adding new features or for bug fixing purposes should not hamper previously working functionalities. Whenever a software is upgraded or modified, a set of test cases are run on each of its functions to assure that the change to that function is not affecting other parts of the software that were previously running flawlessly. For achieving this, all existing test cases need to run as well as new test cases might be required to be created. It is not feasible to reexecute every test case for all the functions of a given software, because if there is a large number of test cases to be run, then a lot of time and effort would be required. This problem can be addressed by prioritizing test cases. Test case prioritization technique reorders the priority in which test cases are implemented, in an attempt to ensure that maximum faults are uncovered early on by the high priority test cases implemented first. In this paper we propose an optimized test case prioritization technique using Ant Colony Optimization (ACO) to reduce the cost, effort and time taken to perform regression testing and also uncover maximum faults. Comparison of different techniques such as Retest All, Test Case Minimization, Test Case Prioritization, Random Test Case Selection and Test Case Prioritization using ACO is also depicted.

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8

Rothermel, G., and M. J. Harrold. "Analyzing regression test selection techniques." IEEE Transactions on Software Engineering 22, no. 8 (1996): 529–51. http://dx.doi.org/10.1109/32.536955.

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9

Kazmi, Rafaqut, Dayang N. A. Jawawi, Radziah Mohamad, and Imran Ghani. "Effective Regression Test Case Selection." ACM Computing Surveys 50, no. 2 (2017): 1–32. http://dx.doi.org/10.1145/3057269.

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10

Gonsalves, Michelle, and Jyothi Mandala. "Regression Test List Sharding in a Distributed Test Environment." E3S Web of Conferences 399 (2023): 04028. http://dx.doi.org/10.1051/e3sconf/202339904028.

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One of the major issues during the regression test of the new version of Real Time Operating System (RTOS) is the time involved in test case execution. The main reason being a single embedded system device under test (DUT) is used to execute the test list containing several test cases. This traditional method of regression test also leads to wasted productivity of the other devices at hand that could be otherwise used during this regression test. Hence, in this paper, we propose a technique that aims at reducing the overall regression test cycle time of a newer version of a Real Time Operating System (RTOS) by employing a method known as “test-list sharding” in a distributed test environment. In the proposed work, multiple DUTs are connected to the test server via a communication network. The test server executes the test list containing several test cases and performs the test-list sharding, that is, distributing test cases to different DUTs and executing them in parallel. After the test is executed on the DUT, the test results are sent back to the test server which will summarize all the results. In the proposed work, the sharding is done by distributing the test cases without overloading or under loading any of the DUTs. Test list is sharded in such a way that the same tests are not sent to multiple DUTs. The main advantage of the proposed method is that the test sharding can be easily scalable to accommodate any number of devices that can be connected to the test server. Also, the test list sharding is done in a dynamic way so that the tests are distributed to an idle DUT that has completed a test execution and ready for another test to execute. The comparison study of executing a sample test list sequentially on a single DUT and distributed test system with multiple DUTs is performed. Results obtained showed the performance gain in terms of test cycle time reduction, scalability, equal load distribution and effective resource utilization.

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11

Sujata and G.N.Purohit. "TOOL SUPPORT FOR TEST CASE SELECTION IN REGRESSION TESTING." International Journal of Software Engineering & Applications (IJSEA) 2, no. 4 (2021): 23–29. https://doi.org/10.5281/zenodo.4899460.

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Software testing and retesting occurs continuously during the software development lifecycle to detect errors as early as possible. As the software evolves the size of test suites also grows. Due to limited resources, basic problem in regression testing is to select the important test cases from the available test suit. In this test case selection techniques unimportant test cases are discarded to save the time and resources. A number of techniques have been proposed for test case selection. Most of them are code based. So, they have not proved viable in all the situations. In this article we present an approach based on specifications.

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12

Kmenta, Jan, Walter Kramer, and Harald Sonnberger. "The Linear Regression Model Under Test." Journal of the American Statistical Association 84, no. 406 (1989): 618. http://dx.doi.org/10.2307/2289959.

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13

Lee, Lung-Fei. "Specification Test for Poisson Regression Models." International Economic Review 27, no. 3 (1986): 689. http://dx.doi.org/10.2307/2526689.

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14

Leblanc, Frederique, Frederique Leblanc, Олег Витальевич Лепский, and Oleg V. Lepskii. "Test of symmetry in nonparametric regression." Teoriya Veroyatnostei i ee Primeneniya 47, no. 1 (2002): 110–30. http://dx.doi.org/10.4213/tvp3003.

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15

LeBlanc, F., and O. V. Lepski. "Test of Symmetry in Nonparametric Regression." Theory of Probability & Its Applications 47, no. 1 (2003): 34–52. http://dx.doi.org/10.1137/s0040585x97979482.

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16

Collins, Mark F. "A Permutation Test for Planar Regression." Australian Journal of Statistics 29, no. 3 (1987): 303–8. http://dx.doi.org/10.1111/j.1467-842x.1987.tb00747.x.

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17

Cade, Brian S., and Jon D. Richards. "A permutation test for quantile regression." Journal of Agricultural, Biological, and Environmental Statistics 11, no. 1 (2006): 106–26. http://dx.doi.org/10.1198/108571106x96835.

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18

Mansour, Nashat, and Wael Statieh. "Regression Test Selection for C# Programs." Advances in Software Engineering 2009 (July 2, 2009): 1–10. http://dx.doi.org/10.1155/2009/535708.

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We present a regression test selection technique for C# programs. C# is fairly new and is often used within the Microsoft .Net framework to give programmers a solid base to develop a variety of applications. Regression testing is done after modifying a program. Regression test selection refers to selecting a suitable subset of test cases from the original test suite in order to be rerun. It aims to provide confidence that the modifications are correct and did not affect other unmodified parts of the program. The regression test selection technique presented in this paper accounts for C#.Net specific features. Our technique is based on three phases; the first phase builds an Affected Class Diagram consisting of classes that are affected by the change in the source code. The second phase builds a C# Interclass Graph (CIG) from the affected class diagram based on C# specific features. In this phase, we reduce the number of selected test cases. The third phase involves further reduction and a new metric for assigning weights to test cases for prioritizing the selected test cases. We have empirically validated the proposed technique by using case studies. The empirical results show the usefulness of the proposed regression testing technique for C#.Net programs.

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19

Binkley, D. "Semantics guided regression test cost reduction." IEEE Transactions on Software Engineering 23, no. 8 (1997): 498–516. http://dx.doi.org/10.1109/32.624306.

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20

Rothermel, G., R. H. Untch, Chengyun Chu, and M. J. Harrold. "Prioritizing test cases for regression testing." IEEE Transactions on Software Engineering 27, no. 10 (2001): 929–48. http://dx.doi.org/10.1109/32.962562.

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21

Shi, August, Milica Hadzi-Tanovic, Lingming Zhang, Darko Marinov, and Owolabi Legunsen. "Reflection-aware static regression test selection." Proceedings of the ACM on Programming Languages 3, OOPSLA (2019): 1–29. http://dx.doi.org/10.1145/3360613.

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22

Elbaum, Sebastian, Alexey G. Malishevsky, and Gregg Rothermel. "Prioritizing test cases for regression testing." ACM SIGSOFT Software Engineering Notes 25, no. 5 (2000): 102–12. http://dx.doi.org/10.1145/347636.348910.

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23

Panigrahi, Chhabi Rani, and Rajib Mall. "Model-based regression test case prioritization." ACM SIGSOFT Software Engineering Notes 35, no. 6 (2010): 1–7. http://dx.doi.org/10.1145/1874391.1874405.

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24

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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25

Laskar, Mizan R., and Maxwell L. King. "Modified Wald test for regression disturbances." Economics Letters 56, no. 1 (1997): 5–11. http://dx.doi.org/10.1016/s0165-1765(97)00128-6.

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26

Reif, Jiři, and Karel Vlček. "Optimal pre-test estimators in regression." Journal of Econometrics 110, no. 1 (2002): 91–102. http://dx.doi.org/10.1016/s0304-4076(02)00124-0.

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27

Cribari-Neto, Francisco, and Spyros Zarkos. "Improved test statistics for multivariate regression." Economics Letters 49, no. 2 (1995): 113–20. http://dx.doi.org/10.1016/0165-1765(95)00669-7.

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28

Nelson, Lloyd S. "Randomization Test for Linear Correlation/Regression." Journal of Quality Technology 29, no. 3 (1997): 354–56. http://dx.doi.org/10.1080/00224065.1997.11979777.

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29

Park, Dong-Ryeon. "Test for Discontinuities in Nonparametric Regression." Communications for Statistical Applications and Methods 15, no. 5 (2008): 709–17. http://dx.doi.org/10.5351/ckss.2008.15.5.709.

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30

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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31

Rastogi, Nitin, Mona Jain, and Saran Prasad. "NTR - New Test Runner, A Test Regression Runner and Analyzer." International Journal of Computer Applications 1, no. 14 (2010): 47–52. http://dx.doi.org/10.5120/303-469.

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32

Henderson, Daniel J. "A test for multimodality of regression derivatives with application to nonparametric growth regressions." Journal of Applied Econometrics 25, no. 3 (2009): 458–80. http://dx.doi.org/10.1002/jae.1099.

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33

Alghalith, Moawia. "The perfect regression and causality test: A solution to regression problems." Biometrical Letters 55, no. 1 (2018): 45–48. http://dx.doi.org/10.2478/bile-2018-0004.

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Summary We introduce a method that eliminates the specification error and spurious relationships in regression. In addition, we introduce a test of strong causality. Furthermore, hypothesis testing (inference) becomes almost unneeded. Moreover, this method virtually resolves error problems such as heteroscedasticity, autocorrelation, non-stationarity and endogeneity.

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34

Sapra, Sunil. "A regression error specification test (RESET) for the truncated regression model." International Journal of Accounting and Economics Studies 6, no. 2 (2018): 53. http://dx.doi.org/10.14419/ijaes.v6i2.13478.

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While a variety of specification tests are routinely employed to test for misspecification in linear regression model, such tests and their applications to the truncated and censored regression models are uncommon. This paper develops a regression error specification test (RESET) for the truncated regression model as an extension of the popular RESET for the linear regression model (Ramsey (1969)). The two proposed extensions TRESET1 and TRESET2 developed in the paper are applied to labor force participation data from Mroz (1987). The paper studies the empirical size and power properties of the proposed tests via Monte Carlo experiments. Our simulation results suggest that both TRESET tests have reasonably good size and power properties for the truncated regression model in medium to large samples. However, TRESET2 consistently outperforms TRESET1 both in terms of empirical size and power in our experiments.

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35

Kieu, Thinh, Phong Luu, and Noah Yoon. "Multiple linear regression: Identify potential health care stocks for investments using out‐of‐sample predictions." Teaching Statistics 42, no. 3 (2020): 98–107. http://dx.doi.org/10.1111/test.12233.

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36

Ashis, Kr. Mukherjee, and Laha Moumi. "PROBLEM OF AUTOCORRELATION IN LINEAR REGRESSION DETECTION AND REMEDIES." International Journal of Multidisciplinary Research and Modern Education 5, no. 1 (2019): 105–10. https://doi.org/10.5281/zenodo.2656268.

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In the classical linear regression model we assume that successive values of the disturbance term are temporarily independent when observations are taken over time. But when this assumption is violated then the problem is known as Autocorrelation. When the disturbance term exhibits serial correlation, the value of the standard error of the parameter estimates are affected and the predictions based on ordinary least square estimates will be inefficient. So we cannot estimate the correct values of the parameters and the estimates are biased. In this study main focus is given on how one can detect the problem of autocorrelation and how the problem can be solved so as to we can estimate the values of the parameters correctly that are best, linear and unbiased. To explain the procedure of detection of autocorrelation and its remedial measure we take an example on indices of real compensation per hour and output per hour in the business sector of the U.S economy for the period 1960-2005. We use Run Test and Durbin-Watson test to detect the problem of autocorrelation, and then explain the procedure how the problem can be solved.

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37

Maspupah, Asri, and Akhmad Bakhrun. "PERBANDINGAN KEMAMPUAN REGRESSION TESTING TOOL PADA REGRESSION TEST SELECTION: STARTS DAN EKSTAZI." JTT (Jurnal Teknologi Terapan) 7, no. 1 (2021): 59. http://dx.doi.org/10.31884/jtt.v7i1.319.

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Regression testing as an essential activity in software development that has changed requirements. In practice, regression testing requires a lot of time so that an optimal strategy is needed. One approach that can be used to speed up execution time is the Regression Test Selection (RTS) approach. Currently, practitioners and academics have started to think about developing tools to optimize the process of implementing regression testing. Among them, STARTS and Ekstazi are the most popular regression testing tools among academics in running test case selection algorithms. This article discusses the comparison of the capabilities of the STARTS and Ekstazi features by using feature parameter evaluation. Both tools were tested with the same input data in the form of System Under Test (SUT) and test cases. The parameters used in the tool comparisons are platform technology, test case selection, functionality, usability and performance efficiency, the advantages, and disadvantages of the tool. he results of the trial show the differences and similarities between the features of STARTS and Ekstazi, so that it can be used by practitioners to take advantage of tools in the implementation of regression testing that suit their needs. In addition, experimental results show that the use of Ekstazi is more precise in sorting out important test cases and is more efficient, when compared to STARTS and regression testing with retest all.

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38

Priya, G. Geetha, and B. Narendra Kumar Rao. "GUI Test Script Repair in Regression Testing." International Journal of Emerging Research in Management and Technology 6, no. 6 (2018): 318. http://dx.doi.org/10.23956/ijermt.v6i6.289.

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GUI testing is a process of testing a software application or functionality of GUI. It is defined as an interface between user and software which provides an easy way to interact with the system. GUI plays an important role in software engineering. GUI-based application requires that a test case consists of sequences of user actions/events to be executed. Selenium is an open-source tool for testing GUI Application by executing test cases whether the GUI Application is working properly or not. In this, we present test script repair technique to repair test cases. Test script repair technique uses reverse engineering process for creating the test script. Test script repair consists of three stages; they are Ripping, Mapping and Repairing. In ripping stage, there are two relationships for representing event interaction of GUI Application. During ripping we know the location of each widget. In mapping stage,original GUI events are mapped to an event-flow graph (EFG). In repairing stage, Event flow graph uses repairing transformations and human input to modified script to repair the test cases, and synthesizes a new “repaired” test script. During this process, test script repair uses GUI objects for yielding a final test script that can be executed using selenium tool to validate the GUI Application. An experiment using selenium tool to test, test cases suggests that it is effective in that unusable test scripts are repaired. Annotations significantly reduced the human cost to repair test cases.

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39

Falah, Bouchaib. "A Complexity Based Regression Test Selection Strategy." Computer Science & Engineering: An International Journal 2, no. 5 (2012): 1–13. http://dx.doi.org/10.5121/cseij.2012.2501.

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40

Malhotra, Ruchika, Arvinder Kaur, and Yogesh Singh. "A Regression Test Selection and Prioritization Technique." Journal of Information Processing Systems 6, no. 2 (2010): 235–52. http://dx.doi.org/10.3745/jips.2010.6.2.235.

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41

King, Maxwell L. "An Alternative Test for Regression Coefficient Stability." Review of Economics and Statistics 69, no. 2 (1987): 379. http://dx.doi.org/10.2307/1927249.

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42

Harrison Oghenekevwe, Etaga, Aforka Kenechukwu Florence, Awopeju Kabiru Abidemi, and Etaga Njideka Cecilia. "Poisson Ridge Regression Estimators: A Performance Test." American Journal of Theoretical and Applied Statistics 10, no. 2 (2021): 111. http://dx.doi.org/10.11648/j.ajtas.20211002.13.

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43

Li, Zheng, Mark Harman, and Robert M. Hierons. "Search Algorithms for Regression Test Case Prioritization." IEEE Transactions on Software Engineering 33, no. 4 (2007): 225–37. http://dx.doi.org/10.1109/tse.2007.38.

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44

Zayed, Hany, and Dana Quade. "A resistant test for simple linear regression." Journal of Nonparametric Statistics 10, no. 2 (1999): 183–96. http://dx.doi.org/10.1080/10485259908832759.

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45

Durot, Cécile, and Anne-Sophie Tocquet. "Goodness of fit test for isotonic regression." ESAIM: Probability and Statistics 5 (2001): 119–40. http://dx.doi.org/10.1051/ps:2001105.

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46

Pei, Yu-Fang, Lei Zhang, Jianfeng Liu, and Hong-Wen Deng. "Multivariate Association Test Using Haplotype Trend Regression." Annals of Human Genetics 73, no. 4 (2009): 456–64. http://dx.doi.org/10.1111/j.1469-1809.2009.00527.x.

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47

Sapra, S. K. "Pre-test estimation in Poisson regression model." Applied Economics Letters 10, no. 9 (2003): 541–43. http://dx.doi.org/10.1080/1350485032000100215.

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48

Suits, L. D., T. C. Sheahan, Pérsio L. A. Barros, and Paulo R. O. Pinto. "Oedometer Consolidation Test Analysis by Nonlinear Regression." Geotechnical Testing Journal 31, no. 1 (2008): 101007. http://dx.doi.org/10.1520/gtj101007.

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49

Durot, Cécile, and Anne-Sophie Tocquet. "Goodness of fit test for isotonic regression." Comptes Rendus de l'Académie des Sciences - Series I - Mathematics 327, no. 2 (1998): 199–204. http://dx.doi.org/10.1016/s0764-4442(98)80089-x.

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50

Mondal, Shouvick, and Rupesh Nasre. "Hansie: Hybrid and consensus regression test prioritization." Journal of Systems and Software 172 (February 2021): 110850. http://dx.doi.org/10.1016/j.jss.2020.110850.

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