Test case generating method based on modified particle swarm algorithm

Abstract

The invention belongs to the field of software test and particularly relates to a test case generating method based on a modified particle swarm algorithm, comprising: introducing a learning factor with a weight function; allowing the learning factor to change in gradient increase or decrease manner correspondingly along with linear or nonlinear changes of inertial weight; balancing global searching and local mining capacities of the algorithm through the interaction of the learning factor and the weight function. Secondary search and reverse learning are introduced herein, so that solving precision can be improved and improvements are brought to population diversity and the like. A design method of a fitness function is analyzed at a test case generating module, excellence degrees of different branch nodes are considered, and more reasonable fitness function evaluation is designed.

Description

technical field [0001] The invention belongs to the field of software testing, and in particular relates to a test case generation based on an improved particle swarm algorithm. Background technique [0002] In the field of software engineering, software testing is the core of software quality assurance. It needs to have three main characteristics: first, high error detection capability, second, low cost consumption, and third, wide applicability. The basic principle of software testing is to provide a set of representative input data to a copy of the program, run this copy in a given environment, and perform appropriate inspection and analysis on the output of the program. To catch all bugs, "exhaustive testing" would be necessary, which is impossible. And with the continuous development of computer technology, the scale of software continues to expand, and at the same time, the needs of users are getting higher and higher, and a series of problems arise. For example, sof...

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Problems solved by technology

[0005] The purpose of the present invention is based on the standard particle swarm algorithm, aiming at the defects in the particle swarm algorithm and the unreasonable design of the existing fitness function, etc., introducing a learning factor with a weight function, searching again, and reverse learning , improving the standard particle swarm algorithm can effectively improve the efficiency and accuracy of the particle swarm algorithm, and design a more reasonable fitness function evaluation according to the pros and cons of different branch nodes

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