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Embedded Software Test Data Generation Method Based on Fuzzy Genetic Algorithm

A fuzzy genetic algorithm and embedded software technology, applied in software testing/debugging, genetic modeling, etc., can solve the problems of large scale of test data sets and long generation time, so as to improve the probability of fault detection, reduce the generation time, and reduce the scale Effect

Active Publication Date: 2017-10-27
HARBIN INST OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In order to solve the problem that the test data set generated by the existing test data generation method has a relatively large scale and the generation time is long

Method used

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  • Embedded Software Test Data Generation Method Based on Fuzzy Genetic Algorithm
  • Embedded Software Test Data Generation Method Based on Fuzzy Genetic Algorithm
  • Embedded Software Test Data Generation Method Based on Fuzzy Genetic Algorithm

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Experimental program
Comparison scheme
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specific Embodiment approach 1

[0025] Specific embodiment one: the embedded software test data generation method based on fuzzy genetic algorithm, related concepts are as follows:

[0026] (1) The related concepts of genetic algorithm are as follows:

[0027] (1) Chromosome

[0028] A piece of test data T=(a 1 ,a 2 ,...,a k ) can be considered as a chromosome, a ip ∈[0,v ip -1], (ip=1,2,...,k), the value a of the ipth parameter in the test data T ip Considered to be the ipth gene on the chromosome, set v ip is the gene pool to which the gene belongs, and k is the number of genes on a chromosome; the gene at a certain position in the chromosome will select a new gene from the gene pool to mutate;

[0029] (2) Adaptive value function

[0030] Assume that the current existing test data set is A, and the set of all t-dimensional interactions not covered by A is Q, that is I is the t-dimensional interaction, H t The test system of embedded software is all t-dimensional interaction, Test data T in A...

specific Embodiment approach 2

[0089] Specific implementation mode two: in the step 1.4 described in this implementation mode, the method of fuzzy reasoning is used to solve p c , including the following steps:

[0090] 1.4.1: Fuzzification: Calculate the entropy and discrete membership degree of the population according to the membership function according to the population set, and activate the fuzzy rules according to the entropy and discrete membership degree values;

[0091] 1.4.2: Fuzzy reasoning: According to the entropy and discrete degree membership value of the population, infer the membership degree of the activated fuzzy rules according to the minimum value method;

[0092] 1.4.3: Defuzzification: According to the degree of membership of different fuzzy rules, the weighted average method is used Accurate calculation of crossover probabilities.

[0093] Other steps and specific parameters are the same as those in the first embodiment.

specific Embodiment approach 3

[0094] Specific implementation mode three: the process of calculating the entropy and dispersion degree of membership of the population in step 1.4.1 described in this implementation mode is:

[0095] If there are R subsets in the mth generation population, The number of individuals contained in each subset is and B m is the set of the mth generation population, then the population entropy is defined as:

[0096]

[0097] In the formula: ir r ∈[1,R]; N is the size of the population; when R=1, S N =0; when R=N, S N = 1;

[0098] According to the test data T 1 and T 2 Dispersion between Define the dispersion of the population as:

[0099]

[0100] In the formula: ir 1 ,ir 2 ∈[1,R] and ir 1 ≠ir 2 as a subset and Dispersion The weight of , when R=1, D N =0; when R=N, D N =1.

[0101] Other steps and specific parameters are the same as those in Embodiment 1 or Embodiment 2.

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Abstract

The invention relates to a method for generating test data of embedded software based on fuzzy genetic algorithm, relating to a method for generating test data. In order to solve the problem of long generation time caused by the large test data set generated by the existing test data generation method. The present invention improves the genetic algorithm, utilizes the fuzzy control method to adaptively control the selection of the genetic operator in the genetic process through the population entropy and dispersion, increases the crossover probability and the mutation probability when the population diversity becomes poor, and makes the population toward Evolve in the direction of the global optimum to reduce the size of the test data; then use the ant colony algorithm to sort the generated combined test data according to the larger dispersion, so as to increase the "distance" between the adjacent test data values ​​of the test parameters , select the test data sort with large dispersion degree from the optimal path sort of all combined test data as the final embedded software test data output. The invention is suitable for generating embedded software test data.

Description

technical field [0001] The invention relates to a method for generating test data. Background technique [0002] Software errors are usually caused by the interaction of a few parameters, which are related to the sequence of values ​​of some input parameters, especially when the value of parameters jumps, it is more likely to cause software errors. The study found that the software errors caused by a single parameter accounted for only 20%-40% of the total, while the software errors caused by the interaction of two parameters could reach 70% of the total, and the software errors caused by three parameters could reach 90% of the total. %about. As the number of parameters increases, the scale of test data and the complexity of algorithms increase exponentially. Therefore, pairwise combination testing technology has always been a research hotspot in the field of combination testing. [0003] As a special form of software, embedded software has the characteristics of strong re...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F11/36G06N3/12
Inventor 魏长安王建峰盛云龙姜守达
Owner HARBIN INST OF TECH
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