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Software defect prediction method based on complex weighted software network

A software defect prediction and software technology, applied in software testing/debugging, probability network, based on specific mathematical models, etc., to achieve the effects of accurately establishing prediction models, improving performance, and improving prediction accuracy

Active Publication Date: 2018-02-06
BEIJING INSTITUTE OF TECHNOLOGYGY
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Problems solved by technology

[0012] The invention proposes a software defect prediction method based on a complex weighted software network graph, which can improve the defect prediction accuracy for large-scale complex software

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  • Software defect prediction method based on complex weighted software network
  • Software defect prediction method based on complex weighted software network
  • Software defect prediction method based on complex weighted software network

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Embodiment Construction

[0037] The following takes the defect prediction for the open source software project Firefox as an example to give a detailed description of the defect prediction process.

[0038] The specific flow of the present invention is as follows figure 2 Shown: A software defect prediction method based on complex weighted software network, including the following steps:

[0039] Step 1. Build a complex weighted software network graph of the predicted software

[0040] For the open source software Firefox, the granularity of the software module is selected as class, and a class-based complex weighted network graph of Firefox is established.

[0041] Assuming that there are three classes A, B, and C in the software source code of Firefox, the number of methods contained in these three classes and the dependencies between the methods are as follows image 3 As shown, the weights between the three classes A, B, and C can be calculated as

[0042] From this, the software network dia...

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Abstract

The invention provides a software defect prediction method based on a complex weighted software network diagram. The defect prediction precision of large-scale complex software can be improved. The method comprises the following steps that firstly, the complex weighted software network diagram is established according to predicted software; secondly, the network attribute value of each node in thecomplex weighted software network diagram established in the first step is determined; thirdly, all software defects aiming at the prediction software are collected from a disclosed software defect library, a historical defect library of the software is established, and defect labels of each software module are marked in the historical defect library; fourthly, the network attribute value of eachnode determined in the second step serves as input of a machine learning algorithm, the defect labels marked in the third step serve as output of the machine learning algorithm, the machine learningalgorithm is trained and tested, and according to the performance evaluation index of the machine learning algorithm, an optimal prediction model can be determined.

Description

technical field [0001] The invention relates to a software defect prediction method based on a complex weighted software network, and belongs to the technical field of software quality assurance. Background technique [0002] At present, the commonly used software defect prediction adopts machine learning method to establish a software defect prediction model. The establishment process is as follows: figure 1 shown: [0003] ①The establishment of the measurement element of the software source code module: for the software source code module of the predicted software, the quality index to measure the software source code module is established. [0004] A software source code module is a piece of code that can be defined by itself. The measurement meta-indicators currently used are mainly aimed at two aspects of software: one is for the source code itself, focusing on attributes such as the code size and inherent complexity of the program module, for example, the CK index fo...

Claims

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Application Information

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IPC IPC(8): G06F11/36G06N7/00G06N99/00
CPCG06F11/3608G06N20/00G06N7/01
Inventor 危胜军何涛单纯胡昌振
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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