Semantic mutation operator based test case generation and optimization method

A test case generation and test case technology, which is applied in software testing/debugging, computing, error detection/correction, etc., can solve the problems of high mutation cost and insufficient resource consumption test cases, so as to overcome high consumption and improve Effects of Efficiency and Coverage

Inactive Publication Date: 2016-08-17
北京京航计算通讯研究所
View PDF2 Cites 18 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In order to overcome the shortcomings of high mutation cost, serious resource consumption and insufficient sufficiency of generated test cases in the existing test case generation method based on grammatical mutation, the present invention proposes a test case generation and optimization method based on semantic mutation operator

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Semantic mutation operator based test case generation and optimization method
  • Semantic mutation operator based test case generation and optimization method
  • Semantic mutation operator based test case generation and optimization method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0053] Now in conjunction with embodiment, accompanying drawing, the present invention will be further described:

[0054] refer to figure 1 . The test case generation and optimization method based on semantic mutation operator mainly includes the following steps:

[0055] 1. Generate semantic variants

[0056] The generation of semantic variants takes the tested source program as input, applies the semantic mutation operator to the source program, and generates corresponding semantic variants. So. The process first depends on the design and implementation of semantic mutation operators. Specifically, we define the program description language as N, the program semantics as L, and the program execution behavior as (N, L). Different from the traditional grammatical mutation operation (N, L) → (N’, L), the operation mode of the semantic mutation operator is (N, L) → (N, L’). If a test case t is executed before and after the change, and the behavior of the program is inconsi...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention relates to a semantic mutation operator based test case generation and optimization method. The method is characterized by mainly including test case generation and initial test case set optimization, test case generation refers to that semantic mutation operators and mixed execution are combined to generate initial test case sets high in coverage rate as far as possible, and initial test case set optimization includes: capturing operation states and fault detect results of the initial test case sets in semantic mutant execution, optimizing according to related indexes provided by the method, and assessing the optimized test case sets.

Description

technical field [0001] The invention belongs to a test case generation and optimization method in software testing, and relates to a mutation test method, a test case generation method, a test case set optimization method and the like. Background technique [0002] The document "Lingming Zhang, Tao Xie, Lu Zhang Test Generation via Dynamic Symbolic Execution for Mutation Testing, ICSM'10 Proceedings of the 2010 IEEE International Conference on Software Maintenance, Pages1-10" discloses a test case generation based on the combination of mutation testing and dynamic symbolic execution method. This method collects the execution path of randomly generated test cases, inverts the last predicate on the execution path, and generates a new path condition. If the execution path does not exist, call the constraint solver to solve and generate new test cases. If it exists, negate the predicate before it, and so on. The test case generation process using dynamic symbolic execution en...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06F11/36
CPCG06F11/3676G06F11/3684
Inventor 郑炜王育杨喜兵吴潇雪
Owner 北京京航计算通讯研究所
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products