Reactor protection system software test-case selection based on input-profile considering concurrent events and uncertainties

2015 ◽  
Vol 53 (8) ◽  
pp. 1077-1085
Author(s):  
M. Khalaquzzaman ◽  
Seung Jun Lee ◽  
Jaehyun Cho ◽  
Wondea Jung
Author(s):  
Victor Cheruiyot ◽  
Baidya Nath Saha

Testing is conducted after developing each software to detect the defects which are then removed. However, it is very difficult task to test a non-trivial software completely. Hence, it’s important to test the software with important test cases. In this research, we developed a machine learning based software test case selection strategy for regression testing. To develop the method, we first clean and preprocess the data. Then we convet the categorical data to its numerical value. The we implement a natural language processing to calculate bag of features for text feature such as testcase title. We evaluate different machine learning models for test case selection. Experimental results demonstrate that machine learning based models can aovid manual labour of the domain experts for test case selection.


2021 ◽  
Vol 16 (11) ◽  
pp. 40-45
Author(s):  
Lennart Vater ◽  
Andreas Pütz ◽  
Levasseur Tellis ◽  
Lutz Eckstein

Author(s):  
Juan C. Burguillo-Rial ◽  
Manuel J. Fernández-Iglesias ◽  
Francisco J. González-Castaño ◽  
Martín Llamas-Nistal

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