MC/DC test data automatic generation method based on genetic algorithm

Abstract

The invention discloses a MC / DC (Modified Condition Decision Coverage) test data automatic generation method based on a genetic algorithm, comprising the following steps of: statically analyzing a tested program to generate a control flow graph, a data flow graph, an abstract syntax tree and an abstract analysis tree; generating a MC / DC test case expected result set; executing code instrumentation on the tested program; constructing a fitness function; randomly generating the test data, and checking whether the test data satisfies an expected execution path; and obtaining proper test data through genetic operations, such as selection, crossing, mutation and the like of the genetic algorithm. In the method, for construction of the fitness function, optimization of approximate-level fitness evaluation by a method of obtaining a control node directly or indirectly influencing defective node traversing through data dependency is proposed according to the thought of a chaining method and based on the conventional fitness function. The method has greater practical value for testing a system with complex logical relations.

Description

technical field [0001] The invention belongs to the technical field of software testing, in particular to a method for automatically generating MC / DC test data based on a genetic algorithm, in particular to a method for automatically generating test data satisfying MC / DC coverage (correction condition determination coverage) in engineering testing. Background technique [0002] Automatic test data generation technology refers to the automatic construction of test input data through specific algorithms based on software specifications or program structures. Trustworthiness. Genetic algorithm is an adaptive artificial intelligence technology that simulates biological evolution process and mechanism to solve extreme value problems. It has unique advantages in solving high-complexity problems such as large space and nonlinear problems. [0003] The idea of ​​dynamically executing programs to generate test data was first proposed by Miller and Spooner, and later scholars did a l...

AI Technical Summary

Problems solved by technology

Aiming at the defect that the traditional fitness function in the genetic algorithm only relies on the control dependencies between the nodes in the control flow graph, and does not consider the data dependencies inside the program under test, it is proposed to use the link method to collect directly or indirectly affect the traversal of problem nodes through data dependencies. control nodes, and combine it with the traditional fitness function, which is good at representing control dependencies, to construct a new fitness function, so as to overcome the guidance caused by the traditional fitness function ignoring the data dependencies inside the program. The problem of lack of information and search degradation

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