Combined test case generation method based on CS-SPSO algorithm

A technology that combines testing and test cases, applied in software testing/debugging, computing, computing models, etc., to solve the problems of particle swarm optimization, which is easy to fall into local optimum, poor diversity of later evolutionary populations, and slow convergence speed.

A technology that combines testing and test cases, applied in software testing/debugging, computing, computing models, etc., to solve the problems of particle swarm optimization, which is easy to fall into local optimum, poor diversity of later evolutionary populations, and slow convergence speed.

CN110334026AActive Publication Date: 2019-10-15ZHEJIANG SCI-TECH UNIV
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  • Combined test case generation method based on CS-SPSO algorithm
  • Combined test case generation method based on CS-SPSO algorithm
  • Combined test case generation method based on CS-SPSO algorithm

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Embodiment Construction

[0048] The present invention will be further described below in conjunction with the accompanying drawings and through specific embodiments.

[0049] like Figure 1~3 As shown, the combined test case generation method of the present invention combines the class IPO strategy with the CS-SPSO algorithm for combined test case generation, including the steps:

[0050] Step 1: Analyze the actual problem, calculate the number n of factors and the value range D of each factor i ={1,2,...,l i}, and obtain the combined cover set S by analyzing the constraints.

[0051] Step 2: Perform non-incremental sorting on the n factors according to the number of value ranges contained in the n factors, and perform random sorting if the numbers are equal. Combining according to the values ​​of the top two factors and analyzing the constraint conditions, a pairwise combination set S' is obtained, wherein the pairwise combination set S' contains several pairwise combinations.

[0052] Step 3: Ra...

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Abstract

The invention discloses a combined test case generation method based on a CS-SPSO algorithm, and belongs to the field of software testing. The method comprises: obtaining a combination set needing tobe covered through constraint analysis; combining the two factors with the maximum values, and performing constraint analysis to obtain a final combination; determining all combinations of other elements according to a class IPO strategy; dividing the combination into N small populations, and carrying out local search on the N small populations by utilizing the simplified particle swarm, so that the advantages of local search of the simplified particle swarm are fully exerted; and taking the obtained N optimal particles as initial values of a cuckoo algorithm to carry out deep optimization, and generating a single test case. During position updating, a reflecting wall strategy is used for carrying out boundary processing on particle positions, and effective search space is prevented from flying out. The method can be suitable for the coverage tables with different coverage intensities, the scale of the combined test case set is effectively reduced, and the generation efficiency of thecombined test case is greatly improved.

Description

technical field [0001] The invention belongs to the field of software testing, in particular to a method for generating combined test cases based on CS-SPSO algorithm. Background technique [0002] Software testing is an important process in the software life cycle. With the expansion of software scale and increasing complexity, testing will not be able to achieve 100% coverage, so choosing a reasonable and efficient testing method is to save testing costs and improve software quality. The key to quality. As a protocol-based testing method, combined testing has the characteristics of small use case scale and strong error detection ability, and can complete the corresponding testing work at a lower cost. [0003] As a relatively new heuristic search algorithm, the particle swarm optimization algorithm has the characteristics of few parameter settings, fast execution speed, and easy implementation. At present, many scholars are studying the method of particle swarm optimizat...

Claims

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Application Information

Patent Timeline
15 Oct 2019
Publication
CN110334026A
IPC
G06F11/36; G06N3/00
CPC
G06F11/3684; G06N3/006
Inventors
包晓安; 金瑜婷