Journal of Information and Optimization Sciences, Nov 15, 2017
Abstract Regression testing attempts to find new errors after some alterations have been done in ... more Abstract Regression testing attempts to find new errors after some alterations have been done in the software. Previously run test cases are re-executed to check whether historical faults have re-emerged or to validate the functionality of the software. In the past, several methods have been analyzed for reducing the cost of regression testing, which includes regression test selection, regression test minimization and regression test prioritization. Test Suite Prioritization techniques orders the test cases so that all the test cases are executed on a priority basis. Artificial bee colony (ABC) algorithm is a very powerful and efficient algorithm influenced by the intelligent nature of bees. In this paper, we have applied the ABC algorithm for test suite prioritization and the comparison has been made with Genetic Algorithm and Cuscuta Search. From our experimental results, we can infer that results of Cuscuta Search are low as compared to ABC and GA for small test suites.
Accurate software reliability prediction with a single universal software reliability growth mode... more Accurate software reliability prediction with a single universal software reliability growth model is very difficult. In this ρ aper we reviewed different models which uses computational intelligence for the prediction purpose and describe how these techniques outperform conventional statistical models. Parameters, efficacy measures with methodologies are concluded in tabular form.
Event Driven Software (EDS) testing is a very challenging task as a large number of events can be... more Event Driven Software (EDS) testing is a very challenging task as a large number of events can be invoked by users. So far it is difficult to test all the user inputs invoked, therefore, test case prioritization is essentially required for giving more priority to test cases which reveal higher faults comparatively. We have proposed test case prioritization for EDS: as the Event Type, Interaction of Event, and Coverage of Event. Priority assigned in the proposed model uses these factors in Adaptive Neuro-Fuzzy Inference System (ANFIS) MATLAB Toolbox based on Neuro-Fuzzy logic model. Evaluation and validation will be done using Average Percentage of Fault Detection (APFD). APFD rate for prioritized sequence using the proposed Neuro-Fuzzy logic model exhibited 81% rate, whereas, non-prioritized test sequences showed70% suggesting, thereby, that after prioritization; rate of fault detection has improved considerably. Data shows that proposed Neuro-Fuzzy logic model is apt for Test Case Prioritization of EDS Testing.
International Conference on Computing for Sustainable Global Development, Mar 11, 2015
The evaluation of website has significant role to gain competitive advantage in the market and to... more The evaluation of website has significant role to gain competitive advantage in the market and to improve overall quality of software. Different web metrics are proposed for the evaluation of websites. The purpose of web metrics or measurements is to obtain better measurements in terms of risk management, reliability forecast, project scheduling, and cost repression. In this paper we have considered usability and structural complexity as a metrics for the evaluation of websites. Structural complexity or website structure is considered as an indicator of complexity of website. These two metrics are evaluated by considering various attributes like Navigation, Page Composition, Effectiveness, Efficiency and Satisfaction. We have used automatic evaluation and manual evaluation procedure to calculate value of these attributes. Further we have proposed a fuzzy model to evaluate usability of websites. For the validation of proposed work we have taken websites in 4 categories these categories are entertainment, college websites, automobiles and information. We have evaluated websites by using our fuzzy model and compared it with webby award data.
Journal of Information and Optimization Sciences, Nov 15, 2017
Abstract Regression testing attempts to find new errors after some alterations have been done in ... more Abstract Regression testing attempts to find new errors after some alterations have been done in the software. Previously run test cases are re-executed to check whether historical faults have re-emerged or to validate the functionality of the software. In the past, several methods have been analyzed for reducing the cost of regression testing, which includes regression test selection, regression test minimization and regression test prioritization. Test Suite Prioritization techniques orders the test cases so that all the test cases are executed on a priority basis. Artificial bee colony (ABC) algorithm is a very powerful and efficient algorithm influenced by the intelligent nature of bees. In this paper, we have applied the ABC algorithm for test suite prioritization and the comparison has been made with Genetic Algorithm and Cuscuta Search. From our experimental results, we can infer that results of Cuscuta Search are low as compared to ABC and GA for small test suites.
Accurate software reliability prediction with a single universal software reliability growth mode... more Accurate software reliability prediction with a single universal software reliability growth model is very difficult. In this ρ aper we reviewed different models which uses computational intelligence for the prediction purpose and describe how these techniques outperform conventional statistical models. Parameters, efficacy measures with methodologies are concluded in tabular form.
Event Driven Software (EDS) testing is a very challenging task as a large number of events can be... more Event Driven Software (EDS) testing is a very challenging task as a large number of events can be invoked by users. So far it is difficult to test all the user inputs invoked, therefore, test case prioritization is essentially required for giving more priority to test cases which reveal higher faults comparatively. We have proposed test case prioritization for EDS: as the Event Type, Interaction of Event, and Coverage of Event. Priority assigned in the proposed model uses these factors in Adaptive Neuro-Fuzzy Inference System (ANFIS) MATLAB Toolbox based on Neuro-Fuzzy logic model. Evaluation and validation will be done using Average Percentage of Fault Detection (APFD). APFD rate for prioritized sequence using the proposed Neuro-Fuzzy logic model exhibited 81% rate, whereas, non-prioritized test sequences showed70% suggesting, thereby, that after prioritization; rate of fault detection has improved considerably. Data shows that proposed Neuro-Fuzzy logic model is apt for Test Case Prioritization of EDS Testing.
International Conference on Computing for Sustainable Global Development, Mar 11, 2015
The evaluation of website has significant role to gain competitive advantage in the market and to... more The evaluation of website has significant role to gain competitive advantage in the market and to improve overall quality of software. Different web metrics are proposed for the evaluation of websites. The purpose of web metrics or measurements is to obtain better measurements in terms of risk management, reliability forecast, project scheduling, and cost repression. In this paper we have considered usability and structural complexity as a metrics for the evaluation of websites. Structural complexity or website structure is considered as an indicator of complexity of website. These two metrics are evaluated by considering various attributes like Navigation, Page Composition, Effectiveness, Efficiency and Satisfaction. We have used automatic evaluation and manual evaluation procedure to calculate value of these attributes. Further we have proposed a fuzzy model to evaluate usability of websites. For the validation of proposed work we have taken websites in 4 categories these categories are entertainment, college websites, automobiles and information. We have evaluated websites by using our fuzzy model and compared it with webby award data.
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