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An intuitive general rank-based correlation coefficient 认领 引用 被引量：3
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作者 Divya PANDOVE Shivani GOEL Rinkle RANI 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2018年第6期699-711,共13页
Correlation analysis is an effective mechanism for studying patterns in data and making predictions.Many interesting discoveries have been made by formulating correlations in seemingly unrelated data. We propose an al... Correlation analysis is an effective mechanism for studying patterns in data and making predictions.Many interesting discoveries have been made by formulating correlations in seemingly unrelated data. We propose an algorithm to quantify the theory of correlations and to give an intuitive, more accurate correlation coefficient.We propose a predictive metric to calculate correlations between paired values, known as the general rank-based correlation coefficient. It fulfills the five basic criteria of a predictive metric： independence from sample size,value between-1 and 1, measuring the degree of monotonicity, insensitivity to outliers, and intuitive demonstration.Furthermore, the metric has been validated by performing experiments using a real-time dataset and random number simulations. Mathematical derivations of the proposed equations have also been provided. We have compared it to Spearman＇s rank correlation coefficient. The comparison results show that the proposed metric fares better than the existing metric on all the predictive metric criteria. 展开更多
关键词 General rank-based correlation coefficient Multivariate analysis Predictive metric Spearman＇s rankcorrelation coefficient
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A novel strategy for automatic test data generation using soft computing technique 认领 引用 被引量：1
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作者 Priyanka CHAWLA Inderveer CHANA Ajay RANA 《Frontiers of Computer Science》 SCIE EI CSCD 2015年第3期346-363,共18页
Software testing is one of the most crucial and analytical aspect to assure that developed software meets pre- scribed quality standards. Software development process in- vests at least 50% of the total cost in softwa... Software testing is one of the most crucial and analytical aspect to assure that developed software meets pre- scribed quality standards. Software development process in- vests at least 50% of the total cost in software testing process. Optimum and efficacious test data design of software is an important and challenging activity due to the nonlinear struc- ture of software. Moreover, test case type and scope deter- mines the quality of test data. To address this issue, software testing tools should employ intelligence based soft comput- ing techniques like particle swarm optimization （PSO） and genetic algorithm （GA） to generate smart and efficient test data automatically. This paper presents a hybrid PSO and GA based heuristic for automatic generation of test suites. In this paper, we described the design and implementation of the proposed strategy and evaluated our model by performing ex- periments with ten container classes from the Java standard library. We analyzed our algorithm statistically with test ad- equacy criterion as branch coverage. The performance ade- quacy criterion is taken as percentage coverage per unit time and percentage of faults detected by the generated test data. We have compared our work with the heuristic based upon GA, PSO, existing hybrid strategies based on GA and PSO and memetic algorithm. The results showed that the test case generation is efficient in our work. 展开更多
关键词 software testing particle swarm optimization,genetic algorithm soft computing test data generation
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