A program state-oriented fault classification method, mutation testing method and equipment

Abstract

The invention provides a program state-oriented fault classification method, a mutation testing method and a device. The method comprises the following steps of: constructing a fault node set and a state mutation point set for each sentence of a source program to be tested based on a mutation operator corresponding to the syntax of the source program to be tested; constructing a variation flow diagram of the source program to be tested based on the fault node set, the state variation point set, the triggering relationship and the propagation relationship between the state variations of each statement; and according to the direct backward dominance relationship of the variant flow diagram, constructing a fault class dominance tree to perform fault classification on the source program to betested. The invention classifies and sorts the fault programs according to the general fault test principle, and supports multiple types of mutation operators, has good scalability, thereby reducing the test times, optimizing the fault set, simplifying the fault analysis cost, and improving the mutation test efficiency.

Description

technical field [0001] The invention relates to the field of software variation testing, and more specifically, to a program state-oriented fault classification method, variation testing method and equipment. Background technique [0002] Mutation testing is a kind of fault-oriented test evaluation technology. It generates a set of faulty programs for the code under test and runs the test to check the detection ability of the test case set for program faults. However, one problem faced by mutation testing is that a large number of mutations (faulty programs) may be generated, which leads to slow evaluation process and consumes a lot of computing resources and testing costs. In order to optimize test efficiency, existing research programs have proposed three solutions: [0003] 1) Variation sampling: Randomly select a small subset from the full set of variants of the program; the defect of this method is that the reliability of the evaluation results is low; [0004] 2) Sel...

AI Technical Summary

Problems solved by technology

[0003] 1) Variation sampling: Randomly select a small subset from the full set of variants of the program; the defect of this method is that the reliability of the evaluation results is low;

[0004] 2) Select mutation: select a subset from all fault types (mutation operator) to generate a fault program; the defect of this method is to remove a class of faults, which may reduce the effectiveness of the evaluation;

[0005] 3) Variation clustering: This method clusters faults with "similar behavior" into one category, and only generates one variant for each type of fault, thereby reducing the number of tests; " modeling and further discussion

[0009] Experiments show that the implication relationship of faults is sparse, so only a small number of faults can be deleted, and the optimization efficiency of the current technology for mutation testing is not high

Images