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Luay Tahat - One of the best experts on this subject based on the ideXlab platform.
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application of system models in Regression Test suite prioritization
2008Co-Authors: Bogdan Korel, George Koutsogiannakis, Luay TahatAbstract:During Regression Testing, a modified system needs to be reTested using the existing Test suite. Since Test suites may be very large, developers are interested in detecting faults in the system as early as possible. Test prioritization orders Test cases for execution to increase potentially the chances of early fault detection during reTesting. Most of the existing Test prioritization methods are based on the code of the system, but model-based Test prioritization has been recently proposed. System modeling is a widely used technique to model state-based systems. The existing model based Test prioritization methods can only be used when models are modified during system maintenance. In this paper, we present model-based prioritization for a class of modifications for which models are not modified (only the source code is modified). After identification of elements of the model related to source-code modifications, information collected during execution of a model is used to prioritize Tests for execution. In this paper, we discuss several model-based Test prioritization heuristics. The major motivation to develop these heuristics was simplicity and effectiveness in early fault detection. We have conducted an experimental study in which we compared model-based Test prioritization heuristics. The results of the study suggest that system models may improve the effectiveness of Test prioritization with respect to early fault detection.
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model based Regression Test reduction using dependence analysis
2002Co-Authors: Bogdan Korel, Luay Tahat, B VaysburgAbstract:Model based Testing is a system Testing technique used to Test software systems modeled by formal description languages, e.g., an extended finite state machine (EFSM). System models are frequently changed because of specification changes. Selective Test generation techniques are used to Test the modified parts of the model. However, the size of Regression Test suites still may be very large. In this paper, we present a model-based Regression Testing approach that uses EFSM model dependence analysis to reduce Regression Test suites. The approach automatically identifies the difference between the original model and the modified model as a set of elementary model modifications. For each elementary modification, Regression Test reduction strategies are used to reduce the Regression Test suite based on EFSM dependence analysis. Our initial experience shows that the approach may significantly reduce the size of Regression Test suites.
Bogdan Korel - One of the best experts on this subject based on the ideXlab platform.
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application of system models in Regression Test suite prioritization
2008Co-Authors: Bogdan Korel, George Koutsogiannakis, Luay TahatAbstract:During Regression Testing, a modified system needs to be reTested using the existing Test suite. Since Test suites may be very large, developers are interested in detecting faults in the system as early as possible. Test prioritization orders Test cases for execution to increase potentially the chances of early fault detection during reTesting. Most of the existing Test prioritization methods are based on the code of the system, but model-based Test prioritization has been recently proposed. System modeling is a widely used technique to model state-based systems. The existing model based Test prioritization methods can only be used when models are modified during system maintenance. In this paper, we present model-based prioritization for a class of modifications for which models are not modified (only the source code is modified). After identification of elements of the model related to source-code modifications, information collected during execution of a model is used to prioritize Tests for execution. In this paper, we discuss several model-based Test prioritization heuristics. The major motivation to develop these heuristics was simplicity and effectiveness in early fault detection. We have conducted an experimental study in which we compared model-based Test prioritization heuristics. The results of the study suggest that system models may improve the effectiveness of Test prioritization with respect to early fault detection.
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model based Regression Test reduction using dependence analysis
2002Co-Authors: Bogdan Korel, Luay Tahat, B VaysburgAbstract:Model based Testing is a system Testing technique used to Test software systems modeled by formal description languages, e.g., an extended finite state machine (EFSM). System models are frequently changed because of specification changes. Selective Test generation techniques are used to Test the modified parts of the model. However, the size of Regression Test suites still may be very large. In this paper, we present a model-based Regression Testing approach that uses EFSM model dependence analysis to reduce Regression Test suites. The approach automatically identifies the difference between the original model and the modified model as a set of elementary model modifications. For each elementary modification, Regression Test reduction strategies are used to reduce the Regression Test suite based on EFSM dependence analysis. Our initial experience shows that the approach may significantly reduce the size of Regression Test suites.
J Jamrozik - One of the best experts on this subject based on the ideXlab platform.
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genetic parameters for a multiple trait multiple lactation random Regression Test day model in italian holsteins
2007Co-Authors: B L Muir, G J Kistemaker, J Jamrozik, F CanavesiAbstract:Abstract The objectives of this study were to estimate variance components for Test-day milk, fat, and protein yields and average daily SCS in 3 subsets of Italian Holsteins using a multiple-trait, multiple-lactation random Regression Test-day animal model and to determine whether a genetic heterogeneous variance adjustment was necessary. Data were Test-day yields of milk, fat, and protein and SCS (on a log 2 scale) from the first 3 lactations of Italian Holsteins collected from 1992 to 2002. The 3 subsets of data included 1) a random sample of Holsteins from all herds in Italy, 2) a random sample of Holsteins from herds using a minimum of 75% foreign sires, and 3) a random sample of Holsteins from herds using a maximum of 25% foreign sires. Estimations of variances and covariances for this model were achieved by Bayesian methods using the Gibbs sampler. Estimated 305-d genetic, permanent environmental, and residual variance was higher in herds using a minimum of 75% foreign sires compared with herds using a maximum of 25% foreign sires. Estimated average daily heritability of milk, fat, and protein yields did not differ among subsets. Heritability of SCS in the first lactation differed slightly among subsets and was estimated to be the highest in herds with a maximum of 25% foreign sire use (0.19±0.01). Genetic correlations across lactations for milk, fat, and protein yields were similar among subsets. Genetic correlations across lactations for SCS were 0.03 to 0.08 higher in herds using a minimum of 75% or a maximum of 25% foreign sires, compared with herds randomly sampled from the entire population. Results indicate that adjustment for heterogeneous variance at the genetic level based on the percentage of foreign sire use should not be necessary with a multiple-trait random Regression Test-day animal model in Italy.
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random Regression Test day models with residuals following a student s t distribution
2004Co-Authors: J Jamrozik, Ismo Stranden, L R SchaefferAbstract:First-lactation milk yield Test-day records of Canadian Holsteins were analyzed by single-trait random Regression Test-day models that assumed normal or Student's-t distribution for residuals. Objectives were to Test the performance of the robust statistical models that use heavy-tailed distributions for the residual effect. Models fitted were: Gaussian, Student's-t, and Student's-t with fixed number of degrees of freedom (equal to 5, 15, 30, 100 or 1000) for the t distribution. Bayesian methods with Gibbs sampling were used to make inferences about overall model plausibility through Bayes factors, posterior means for covariance components, estimated breeding values for Regression coefficients, solutions for permanent environmental Regressions, and residuals of the models. Bayes factors favored Student's-t model with the posterior mean of degrees of freedom equal to 2.4 over all other models, indicating very strong departure from normality. Number of outliers in Student's-t model was reduced by 35% in comparison with the Gaussian model. Differences in covariance components for Regression coefficients between models were small, and rankings of animals based on additive genetic merit for the first two Regression coefficients (total yield and persistency) were similar. Results from the Gaussian and Student's-t models with fixed degrees of freedom become more alike (smaller departures from normality for Student's-t models) with increasing number of degrees of freedom for the t-distributions. For any pair of Student's-t models, the one with the smaller number of degrees of freedom for the t-distribution was shown to be superior. Similarly, number of outliers increased with increasing degrees of freedom for the t distribution.
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multiple trait random Regression Test day model for production traits
1997Co-Authors: J Jamrozik, L R Schaeffer, Z Liu, G B JansenAbstract:Research on Test day (TD) models began in Canada in 1991. At that time fixed Regressions (within age and season of calving) were used to account for the shape of the lactation curve of dairy cows, but the animal effect was used only to account for differences in height of these curves (Ptak and Schaeffer, 1993). Fixed Regression TD model was applied to Canadian dairy goat data (Schaeffer and Sullivan, 1994) and to Canadian data on somatic cell score (SCS) (Reents et al., 1995a,b). Schaeffer and Dekkers (1994) suggested the possibility of using random Regressions in a linear model (Henderson, 1982) for analysing TD data. Single trait random Regression models were applied to first lactation milk, fat and protein records, with different functions for fixed and random Regressions (Jamrozik and Schaeffer, 1997; Jamrozik et al., 1997a,b). In the simulation study of Kistemaker (1997) random Regression models were significantly better than an analysis of 305d yields in terms of correlation between estimated and true breeding values. A 2-3% increase in accuracy for bulls and 68% for cows was found for first lactation milk yield. Changes occuring in Canadian milk recording system will require an application of multiple trait Test day model in genetic evaluation for dairy production traits. Future milk recording programs would have TD records that have all yield values (milk, fat, protein, SCS) while other TD records might have only milk yield. Thus the multiple trait model for simultaneous analysis of all yields seems to be a logical choice. Also, preliminary work with milk yield in different lactations showed that lactation curves were different between first and second lactation, and again between second and later lactations. Thus, yields in different lactations should also be considered as different traits. The objective of this research was to develop the multiple trait, random Regression TD model for genetic evaluation of dairy bulls and cows for production traits. Both animal genetic and permanent environmental effects were modelled by random Regressions in this model. Some preliminary results are presented.
S Sahaaya Arul A Mary - One of the best experts on this subject based on the ideXlab platform.
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requirement based system Test case prioritization of new and Regression Test cases
2009Co-Authors: R Krishnamoorthi, S Sahaaya Arul A MaryAbstract:Test case prioritization schedules the Test cases in an order that increases the effectiveness in achieving some performance goals. One of the most important performance goals is the rate of fault detection. Test cases should run in an order that increases the possibility of fault detection and also detects the most severe faults at the earliest in its Testing life cycle. Test case prioritization techniques have proved to be beneficial for improving Regression Testing activities. While code coverage based prioritization techniques are found to be studied by most scholars, Test case prioritization based on requirements in a cost effective manner has not been used for studies so far. Hence, in this paper, we propose to put forth a model for system level Test Case Prioritization (TCP) from software requirement specification to improve user satisfaction with quality software that can also be cost effective and to improve the rate of severe fault detection. The proposed model prioritizes the system Test cases based on six factors: customer priority, changes in requirement, implementation complexity, usability, application flow and fault impact. The proposed prioritization technique is experimented in three phases with student projects and two sets of industrial projects and the results show convincingly that the proposed prioritization technique improves the rate of severe fault detection.
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factor oriented requirement coverage based system Test case prioritization of new and Regression Test cases
2009Co-Authors: R Krishnamoorthi, S Sahaaya Arul A MaryAbstract:Test case prioritization involves scheduling Test cases in an order that increases the effectiveness in achieving some performance goals. One of the most important performance goals is the rate of fault detection. Test cases should run in an order that increases the possibility of fault detection and also that detects the most severe faults at the earliest in its Testing life cycle. In this paper, we propose to put forth a model for system level Test case prioritization (TCP) from software requirement specification to improve user satisfaction with quality software that can also be cost effective and to improve the rate of severe fault detection. The proposed model prioritizes the system Test cases based on the six factors: customer priority, changes in requirement, implementation complexity, completeness, traceability and fault impact. The proposed prioritization technique is validated with two different validation techniques and is experimented in three phases with student projects and two sets of industrial projects and the results show convincingly that the proposed prioritization technique improves the rate of severe fault detection.
Jason A Osborne - One of the best experts on this subject based on the ideXlab platform.
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system Test case prioritization of new and Regression Test cases
2005Co-Authors: Hema Srikanth, Laurie Williams, Jason A OsborneAbstract:Test case prioritization techniques have been shown to be beneficial for improving Regression-Testing activities. With prioritization, the rate of fault detection is improved, thus allowing Testers to detect faults earlier in the system-Testing phase. Most of the prioritization techniques to date have been code coverage-based. These techniques may treat all faults equally. We build upon prior Test case prioritization research with two main goals: (1) to improve user-perceived software quality in a cost effective way by considering potential defect severity and (2) to improve the rate of detection of severe faults during system-level Testing of new code and Regression Testing of existing code. We present a value-driven approach to system-level Test case prioritization called the prioritization of requirements for Test (PORT). PORT prioritizes system Test cases based upon four factors: requirements volatility, customer priority, implementation complexity, and fault proneness of the requirements. We conducted a PORT case study on four projects developed by students in advanced graduate software Testing class. Our results show that PORT prioritization at the system level improves the rate of detection of severe faults. Additionally, customer priority was shown to be one of the most important prioritization factors contributing to the improved rate of fault detection.
