Automatic data flow class testing based on 2-step heterogeneous process using evolutionary algorithms

2019 ◽  
Vol 22 (7) ◽  
pp. 1315-1348
Author(s):  
Neetu Jain ◽  
Rabins Porwal ◽  
Sumit Kumar ◽  
Sapna Varshney ◽  
Mukesh Saraswat
Author(s):  
Neetu Jain ◽  
Rabins Porwal

Background: Software testing is a time consuming and costly process. Recent advances in complexity of software have gained attention among researchers towards the automation of generation of test data. Objective: This paper focuses on the structural testing of object oriented paradigm based software and proposes a hybrid approach to automate the class testing applying heuristic algorithms. Method:The proposed algorithm performs data flow testing of classes applying all def-uses adequacy criteria by automatically generating test cases. A nested 2-step methodology is applied using meta-heuristic genetic algorithm and its two variant (GA-variant1 and Ga-variant2) to produce optimized method sequences. Results: An experiment is performed applying proposed algorithm on six test classes. The results suggest that proposed approach with GA-variant1 is better than other techniques in terms of Average d-u coverage and Average iterations.


2004 ◽  
Vol 28 (2) ◽  
pp. 77-84
Author(s):  
Michael S. Grow ◽  
Donglok Kim ◽  
Yongmin Kim

Author(s):  
B. Ralph ◽  
A.R. Jones

In all fields of microscopy there is an increasing interest in the quantification of microstructure. This interest may stem from a desire to establish quality control parameters or may have a more fundamental requirement involving the derivation of parameters which partially or completely define the three dimensional nature of the microstructure. This latter categorey of study may arise from an interest in the evolution of microstructure or from a desire to generate detailed property/microstructure relationships. In the more fundamental studies some convolution of two-dimensional data into the third dimension (stereological analysis) will be necessary.In some cases the two-dimensional data may be acquired relatively easily without recourse to automatic data collection and further, it may prove possible to perform the data reduction and analysis relatively easily. In such cases the only recourse to machines may well be in establishing the statistical confidence of the resultant data. Such relatively straightforward studies tend to result from acquiring data on the whole assemblage of features making up the microstructure. In this field data mode, when parameters such as phase volume fraction, mean size etc. are sought, the main case for resorting to automation is in order to perform repetitive analyses since each analysis is relatively easily performed.


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