Regression testing of the software during its maintenance phase, requires test case prioritization and selection due to the dearth of the allotted time. The resources and the time in this phase are very limited, thus testers tend to use regression testing methods such as test case prioritization and selection. The current study evaluates the effectiveness of testing with two major goals: (1) Least running time and (2) Maximum fault coverage possible. Ant Colony Optimization (ACO) is a well-known soft computing technique that draws its inspiration from nature and has been widely researched, implemented, analyzed, and validated for regression test prioritization and selection. Many versions of ACO approaches have been prolifically applied to find solutions to many non-polynomial time-solvable problems. Hence, an attempt has been made to enhance the performance of the existing ACO_TCSP algorithm without affecting its time complexity. There have been efforts to enhance the exploration space of various paths in each iteration and with elite exploitation, reducing the total number of iterations required to converge to an optimal path. Counterbalancing enhanced exploration with intelligent exploitation implies that the run time is not adversely affected, the same has also been empirically validated. The enhanced algorithm has been compared with the existing ACO algorithm and with the traditional approaches. The approach has also been validated on four benchmark programs to empirically evaluate the proposed Enhanced ACO_TCSP algorithm. The experiment revealed the increased cost-effectiveness and correctness of the algorithm. The same has also been validated using the statistical test (independent t-test). The results obtained by evaluating the proposed approach against other reference techniques using Average Percentage of Faults Detected (APFD) metrics indicate a near-optimal solution. The multiple objectives of the highest fault coverage and least running time were fruitfully attained using the Enhanced ACO_TCSP approach without compromising the complexity of the algorithm.

The objective of this study is to compare the statistical performance of different classification methods on three medical datasets in terms of accuracy, recall, precision and F-score. The comparison is performed by building models using 32 different machine learning-based hybridised techniques and classification algorithms, which yields unbiased, accurate and reproducible results. For this purpose, well-known medical datasets; Wisconsin Breast Cancer dataset, Pima Indians Diabetes dataset and Liver Disorders dataset, are used. Different pre-processing approaches such as missing value imputation, normality test and feature selection are implemented in an open source package R for each dataset. The experimental results indicate an average of 10 runs of 10-fold cross-validation. Promisingly, this comprehensive study analyses the statistical performance of 32 classification techniques on each medical datasets.