Pairwise Testing Example

Testing It ◽  
2011 ◽  
pp. 274-276
Author(s):  
John Watkins ◽  
Simon Mills
Keyword(s):  
Pairwise Testing

Framework of Particle Swarm Optimization Based Pairwise Testing

2011 ◽  
Vol 22 (12) ◽  
pp. 2879-2893 ◽  
Author(s):  
Xiang CHEN ◽  
Qing GU ◽  
Zi-Yuan WANG ◽  
Dao-Xu CHEN
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Pairwise Testing

Abstract TP188: Location of Intracerebral Hemorrhage in a Tri-ethnic Cohort: The ERICH Study

Stroke ◽  
2013 ◽  
Vol 44 (suppl_1) ◽  
Author(s):  
Lee Birnbaum ◽  
Anne Leonard ◽  
Julio Andino ◽  
Charles J Moomaw ◽  
Carl Langfeld ◽  
...  
Keyword(s):  
Intracerebral Hemorrhage ◽  
Univariate Analysis ◽  
Case Fatality ◽  
Final Model ◽  
Location Data ◽  
History Of ◽  
Race Ethnicity ◽  
Pairwise Testing ◽  
Racial Ethnic ◽  
Control Study

Background: The Ethnic/Racial Variations of Intracerebral Hemorrhage (ERICH) is an ongoing case-control study of spontaneous ICH among non-hispanic whites, non-hispanic blacks, and Hispanics. Prior studies have identified hypertension as a greater risk for non-lobar (NL) ICH as compared with lobar (L) ICH. Given the greater reported prevalence of hypertension among black and Hispanic populations, we hypothesized that the location of ICH may differ by race/ethnicity. Methods: At the time of this analysis, we had ICH location data, lobar vs. non-lobar, on 648 subjects. We performed univariate analysis on known and potential predictors of ICH location: age, sex, race/ethnicity, hypertension, diabetes, BMI, creatinine, cholesterol, aspirin use, smoking, alcohol use, caffeine use, and INR. INR was dichotomized at >1.1. After forcing in age, sex, race, history of diabetes, aspirin use and INR, we added significant and near-significant (p<0.2) variables in a stepwise fashion to complete our final logistic regression model. Our outcome measure was lobar ICH. Conditional pairwise testing was performed for race/ethnicity. Results: Of the 648 subjects (mean age 61.12 ± 14.51 years; 39.8% female; 35.0% Hispanic, 26.5% white, 38.4% black), 181 (27.9%) presented with lobar ICH. Hypertension was present in 525 subjects (75.1% L, 83.3% NL; p=.018), diabetes in 152 (26.0% L, 22.5% NL; p=.348), high cholesterol in 244 (45.9% L, 34.5% NL; p=.008), aspirin use in 200 (37.0% L, 28.5% NL; p=.035), and INR >1.1 (24.1% L, 21.8% NL; p=.535) In our final model, race/ethnicity (p<.024) was associated with location of ICH. Furthermore, white race/ethnicity was associated with L ICH, compared with black (b=.57, p=.016) or Hispanic (b=.56, p=.018). Hypertension (b=-0.63, p=.009) was associated with NL ICH, and smoking (b=0.51, p=.007) was associated with L ICH. Discussion: Our results suggest that there are significant racial/ethnic differences in the distribution of lobar and non-lobar ICH. The conditional pairwise testing for race/ethnicity showed a significantly higher rate of lobar ICH in whites, compared with blacks or Hispanics. These findings are intriguing given the differences in case-fatality rates and age at ICH onset.

A Prioritization Method for SPL Pairwise Testing Based on User Profiles

2019 ◽  
Author(s):  
Hirofumi Akimoto ◽  
Yuto Isogami ◽  
Takashi Kitamura ◽  
Natsuko Noda ◽  
Tomoji Kishi
Keyword(s):  
User Profiles ◽  
Prioritization Method ◽  
Pairwise Testing

Software Product Line Testing Based on Feature Model Mutation

2017 ◽  
Vol 27 (05) ◽  
pp. 817-839 ◽  
Author(s):  
Johnny Maikeo Ferreira ◽  
Silvia Regina Vergilio ◽  
Marcos Quinaia
Keyword(s):  
Software Product Line ◽  
Product Line ◽  
Experimental Results ◽  
Feature Model ◽  
Test Case ◽  
Test Cases ◽  
Mutation Operators ◽  
Software Product ◽  
Pairwise Testing ◽  
Feature Management

The Feature Model (FM) is a fundamental artifact of the Software Product Line (SPL) engineering, used to represent commonalities and variabilities, and also to derive products for testing. However, the test of all features combinations (products) is not always possible in practice. Due to the growing complexity of the applications, only a subset of products is usually selected. The selection is generally based on combinatorial testing, to test features interactions. This kind of selection does not consider different classes of faults that can be present in the FM. The application of a fault-based approach, such as mutation-based testing, can increase the probability of finding faults and the confidence that the SPL products match the requirements. Considering that, this paper introduces a mutation approach to select products for the feature testing of SPLs. The approach can be used similarly to a test criterion in the generation and assessment of test cases. It includes (i) a set of mutation operators, introduced to describe typical faults associated to the feature management and to the FM; and (ii) a testing process to apply the operators. Experimental results show the applicability of the approach. The selected test case sets are capable to reveal other kind of faults, not revealed in the pairwise testing.

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