A Construction of Fault Test Constraint Based on the Optimal Fusion Set of Multiple Slices

2013 ◽  
Vol 774-776 ◽  
pp. 1604-1608
Author(s):  
Yi Zhang ◽  
Gang Wang ◽  
Ping Rong Lin

The standard program slicing of different slices is put into the fusion matrix of the optimal fusion to measure the consistent fusion of slices. In the biopsy of the actual fusion process, the slicing techniques with high consistent fusion and balanced fusion distribution are used to reasonably allocate each weight coefficient, and thus the final fusion estimation formula is obtained. We use slice fusion, path conditions, as well as the internal mechanism of software fault trigger and propagation, to construct the test constraint of a fault. It can help to direct high quality test case design and to evaluate the applicability of the adaptive random testing.

2013 ◽  
Vol 380-384 ◽  
pp. 2403-2406
Author(s):  
Wen Hong Liu ◽  
Xin Wu

Failure mode judgment software is widely used in the security control system, it always has higher safety critical level than other softwares. Therefore the testing standards for such software must also be improved. In order to solve the problem of the black box testing for such software, this paper establishment the software fault tree based on the software fault analysis, and use the software fault tree as a test case design tree, then design the test case after obtained the minimal cut sets. Meanwhile, this paper establish the test case design tree for the failure mode misjudgment, and use it as the basis in designing the failure mode misjudgment test cases. We developed the test case assisted design tool based on these methods, and successfully use it in the software testing of the escape software.


2021 ◽  
pp. 1-16
Author(s):  
Shengbing Ren ◽  
Xing Zuo ◽  
Jun Chen ◽  
Wenzhao Tan

The existing Software Fault Localization Frameworks (SFLF) based on program spectrum for estimation of statement suspiciousness have the problems that the feature type of the spectrum is single and the efficiency and precision of fault localization need to be improved. To solve these problems, a framework 2DSFLF proposed in this paper and used to evaluate the effectiveness of software fault localization techniques (SFL) in two-dimensional eigenvalues takes both dynamic and static features into account to construct the two-dimensional eigenvalues statement spectrum (2DSS). Firstly the statement dependency and test case coverage are extracted by the feature extraction of 2DSFLF. Subsequently these extracted features can be used to construct the statement spectrum and data flow spectrum which can be combined into the optimized spectrum 2DSS. Finally an estimator which takes Radial Basis Function (RBF) neural network and ridge regression as fault localization model is trained by 2DSS to predict the suspiciousness of statements to be faulty. Experiments on Siemens Suit show that 2DSFLF improves the efficiency and precision of software fault localization compared with existing techniques like BPNN, PPDG, Tarantula and so fourth.


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