درخت حوزه‌های تخصصی

Now a day's software is the baseline for the success of any organization. There is a huge demand of quality software in the customer-oriented market. Regression testing makes it possible but it’s a costly affair. Regression test suite minimization is way to reduce this cost but it is NP hard problem. This paper proposes an effective approach for regression test suite minimization using Artificial Ecosystem Optimization algorithm. To improve its performance a modified Artificial Ecosystem Optimization algorithm is proposed for Test case minimization. To evaluate the performance of proposed approach experiment is conducted in controlled parameter setting on open-source subject program from SIR repository. The results are collected and analyzed in comparison to existing approaches using statistical test. The test results reflect the superiority of proposed approach.

The purpose of this study is to present a gray marketing model using the role of influencers in social media. This research has been collected in terms of practical purpose and in terms of data collection method, quantitative-qualitative, it means using a questionnaire and interview. The face validity of the questionnaire was confirmed by experienced professors. The reliability of the questionnaire was also assessed using Cronbach's alpha method. The statistical population of the study consists of two sections: food consumers and faculty members, experts who had the most information in the field of gray marketing and the role of  virtual influencers. 384 were selected from the first population after determining the sample size using Cochran's formula. From the second community, 14 people were selected and their opinions were qualitatively analyzed. In quantitative data analysis, the structural equation approach was used and in qualitative data analysis, Grounded theory approach method and in-depth interview were used. Findings of this study were extracted in the qualitative part of the variables of causal factors, contextual factors, intervening factors, and outcomes and the findings of the quantitative section showed that the coefficient of variable path of parallel factors and parallel imports is 0.906 and also t-statistic is 11.328 and the coefficient of variable path of parallel factors and parallel imports is 0.992 and t-statistic is 13.476. The coefficient of variable path of interfering factors and parallel import is 0.591 and t-statistic is 8.017. Also, the coefficient of the variable of consequences and parallel imports is 0.755 and also the t-statistic is 9.705. Finally, the variable path coefficient of strategies and parallel imports is 0.673 and the t-statistic is 8.627.