A Prediction Method of Variation Test Intensity Demand in Evolutionary Environment

Abstract

The invention relates to a method for predicting demand for variation test intensity in an evolutionary environment. Based on feature extraction and error detection numbers, this method constructs feature matrix and intensity vector for model training through data cleaning and feature selection for each version of the software; on this basis, the BP neural network method is used to build a prediction model, and the During construction, the weight of the model is continuously adjusted through the results of signal forward propagation and error backward propagation, so as to learn and generate a variation test intensity demand prediction model BP-Model; finally, input the feature vector of the current software version into BP-Model, and finally generate Mutation testing intensity requirements for the current software version. The purpose of the present invention is to solve the problem that the intensity of mutation testing of new version software is unknown at present, and then help testers formulate reasonable mutation testing requirements, and help testers build effective test case sets within a limited time.

Description

technical field [0001] The invention belongs to the field of software testing and program analysis, and is especially suitable for the field of test adequacy measurement in software testing. A method to determine a more reasonable software variation testing intensity requirement within a short period of time. Background technique [0002] In the process of software evolution, its structure and function will continue to change. For example, fix legacy bugs, add new features, delete obsolete features; another example, change the implementation of features, trigger locations, etc. Through evolution, the software structure becomes more reasonable, and the functions and services are more perfect. At the same time, the test case set of the software also needs to change accordingly. Taking the unit test of an object-oriented program as an example, a unit test case usually includes four components such as initialization, calling, checking, and clearing. When the construction met...

AI Technical Summary

Problems solved by technology

However, it is usually difficult to construct a test case set with the highest mutation score: on the one hand, the variety of mutation operators and the selection of mutation positions make the number of potential mutation programs very large, and corresponding tests should be designed for different mutation programs. Use cases bring a lot of burden to testers; on the other hand, with the continuous advancement of software development concepts, methods, technologies, and tools, and the significant improvement of hardware product performance, the development mode of software products is also more developed from the original order. The waterfall model has gradually evolved into an agile model that emphasizes iteration and gradual progress. As a result, the iteration cycle of each software product is shortened, and the time left for testers is even less.

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