Multi-path coverage method and system combining key point probability and path similarity

Abstract

The invention discloses a multi-path coverage method and system combining key point probability and path similarity. The method comprises the steps: firstly, dividing a theoretical path into an easily-covered path, a difficultly-covered path and an unreachable path; secondly, counting key point probabilities through the easy-to-cover path, calculating contribution degrees of individuals to generated test data according to the probabilities, improving a fitness function by utilizing the contribution degrees, and sorting target paths according to the key point probabilities; and finally, generating test data covering the target path by using a multi-population genetic algorithm, and continuously trying to cover the similar path of the target path after the sub-population covers the current target path in the evolution process. The fitness function is designed according to the probability of the key points, excellent individuals are protected, meanwhile, an individual information sharingstrategy is further perfected, individual resources in the population evolution process are reasonably utilized, it is avoided that too much time is wasted in the sub-population evolution process, andthus the test data evolution generation efficiency is effectively improved.

Description

technical field [0001] The invention relates to the technical field of software testing systems, in particular to a multi-path coverage method and system based on combining key point probability and path similarity. Background technique [0002] In the process of software development, software testing can be regarded as a review of software requirements analysis, design specifications and coding, etc., and is an important stage to ensure software quality. It can be seen that software testing plays a key role in the entire life cycle of software development. In the process of software testing, the prerequisite for the program under test to be able to test is to have suitable test data. Therefore, the generation of test data is an indispensable work in software testing. [0003] In actual software testing, to generate test data that meets the requirements of the program under test, professional testers need to spend a lot of time on experiments, which will undoubtedly consume...

AI Technical Summary

Problems solved by technology

In the automatic generation of multi-path coverage test data, the classical genetic algorithm is often used to solve the problem, but there are still problems such as low multi-path coverage and long test data generation time.

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