Fault prediction method based on software network feature learning

Abstract

The invention discloses a fault prediction method based on software network feature learning, and belongs to the field of software complicated networks. The method comprises the steps that source codes and defect information of each version of target software are obtained to construct a software defect network; multi-layered structure metric elements aiming at internal features, local features andglobal features of software defect network nodes are designed to construct a defect prediction data set; preprocessing is conducted on a defect prediction data set by using a maximized minimum methodand a PCA dimensionality reduction method; the best defect prediction model suitable for the software is established by using prediction model preference method of a dynamic prediction threshold filtering algorithm, and defect prediction effects and scores are given. According to the method, a complicated network technology is introduced to the defect prediction, a set of matric elements capableof reflecting the local and global features of defects in the software is designed by using complicated network matric parameters, the most suitable defect prediction model of the target software canbe selected to perform defect and fault prediction on the target software, and manpower and time cost is reduced to the most extent.

Description

technical field [0001] The invention is applied to the field of complex software networks, and is a fault prediction method based on software network feature learning. Background technique [0002] With the rapid development of network and information technology in the past few decades, software, as a carrier for computers to realize various functions and assist people in various activities, plays an important role in all walks of life in the world today. An efficient and safe software system is highly dependent on software quality, and software defects that affect software quality are the root causes of system errors, failures, crashes, and even disasters. Therefore, software defect prediction has become a hot research field in recent years. At present, carrying out a large number of software tests is an important means to improve software reliability, and the development costs for supporting software tests are hundreds of millions every year. However, with the increasing...

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Problems solved by technology

[0020] The present invention is aimed at the existing defect prediction mainly by analyzing the software code or the development process, using the statistical feature data on the file or class related to the software defect as the measurement element, ignoring the macro integrity of the software program to a certain extent, To deal with issues such as the correlation between local defects and surrounding program elements, complex network technology is introduced into defect prediction. By establishing a software network model and using complex network measurement parameters to design a set of measurement elements that can reflect the local and overall characteristics of defects in the software, And propose a prediction model optimization method based on dynamic prediction threshold filtering algorithm

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