Self-adaptive random test method based on iteration area equipartition and positioning

Abstract

The invention discloses a self-adaptive random test method based on iteration area equipartition and positioning, which is used for reducing blind generation of test cases in a random test and improving the efficiency of the random test. The method mainly comprises the steps that 1, determining the range of an input domain; 2, randomly generating a first test case in an input domain; 3, equally dividing the input domain, randomly selecting a sub-domain at the same time, then equally dividing (hypothetical equally dividing) again, randomly generating test cases for the sub-domains which are equally divided again, and forming a candidate test case set; and 4, aiming at the candidate test case set, determining an adjacent region set, forming an executed test case set by the test cases in theadjacent region, and determining the next test case to be executed by adopting an FSCS _ ART (self-adaptive random algorithm of a candidate set with a fixed size). The process is continuously repeateduntil program errors are found. Experimental verification proves that compared with a random test method, the method has the advantage that the performance is improved by 30%.

Description

technical field [0001] The invention proposes an adaptive random testing method based on iterative area equalization and positioning, which is used for generating test cases and belongs to the technical field of test automation. Background technique [0002] With the continuous expansion of the software market, the scale of software is increasing day by day, and the software functions are becoming more and more complex. How to ensure the quality of software products has become an important research focus, and software testing has undoubtedly become an important link to ensure software quality. Aiming at software testing, researchers have proposed many software testing techniques, and among them, random testing has received more and more attention due to its simple concept and automation. The existing random testing processes are as follows: figure 1 shown. [0003] Random testing can detect errors that people can't expect (and this kind of error detection can only be done b...

AI Technical Summary

Problems solved by technology

[0006] The traditional FSCS_ART (Adaptive Random Test of Fixed Size Candidate Set) method achieves the uniform distribution of test cases by continuously calculating the distance, but this method has a large time overhead, and the adaptive random test method through area division , although the time overhead is small, but the performance is not good. Therefore, the present invention proposes an adaptive random testing method based on iterative equalization and positioning. Combining the two ideas, it not only saves time overhead, but also improves the adaptive random testing method based on area division. Performance of random testing methods

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