Software quality prediction method, apparatus, terminal, and computer-readable storage medium

Abstract

The embodiment of the invention provides a software quality prediction method, a device, a terminal and a computer-readable storage medium. The method comprises the following steps: acquiring characteristic data of a plurality of categories of software to be predicted in a software development flow; preprocessing the characteristic data of each kind and inputting the same into the quality prediction model. Quality prediction model is based on back propagation neural network and multi-class historical data. The quality prediction model processes all kinds of characteristic data and outputs theprediction results of the software to be predicted. The embodiment of the invention synthesizes the characteristic data of each category in the software development flow according to the related indexes affecting the software quality, and carries out the software quality prediction quickly and comprehensively, so as to ensure the prediction accuracy.

Description

technical field [0001] The present invention relates to the technical field of information security, in particular to a software quality prediction method, device, terminal and computer-readable storage medium. Background technique [0002] In the prior art, there is no method for predicting software quality by integrating various index factors. All of them use simple index factors or human intervention to predict software quality. Since there are many factors that affect software quality, it is unreasonable to simplify the prediction of software quality to directly quantified indicators such as the number of lines of code modification or code coverage, because these factors cannot reflect the real software quality. The error is large. However, the method of relying on software testers is poor in operability, and the test results of personnel with different experience are very different. With the great abundance of Internet products, it is unrealistic to simply test by hu...

AI Technical Summary

Problems solved by technology

Since there are many factors that affect software quality, it is unreasonable to simplify the prediction of software quality to directly quantified indicators such as the number of lines of code modification or code coverage, because these factors cannot reflect the real software quality. large error

However, the method of relying on software testers is poor in operability, and the test results of personnel with different experience are very different.

With the great abundance of Internet products, in the face of massive data, it is unrealistic to conduct testing solely by humans.

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