Classification method for airborne system software defects

A technology for software defects and airborne systems, applied in text database clustering/classification, natural language data processing, special data processing applications, etc., can solve the problem of large manpower and resources, affecting airborne software, and airborne software testing efficiency Inefficient and other problems, to achieve the effect of improving efficiency and quality, accurate classification and prediction

Pending Publication Date: 2022-02-25
BEIHANG UNIV
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Problems solved by technology

[0003] Compared with general software defects, airborne software defects have more complex domain characteristics. Existing text segmentation techniques (such as conditional random field model) or general topic acquisition models (such as Latent Dirichlet Allocation topic model, namely latent Dirichlet Lai distributed topic model, hereinafter referred to as the LDA topic model), it is difficult to accurately segment the text or obtain the topic of the airborne software defect data
In addition, the existing data classification technology based on topic model is mostly unsupervised learning technology, which has less

Method used

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  • Classification method for airborne system software defects
  • Classification method for airborne system software defects
  • Classification method for airborne system software defects

Examples

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Embodiment Construction

[0071] (1) Implementation preparation

[0072] The object selected in this example is a certain type of airborne display and control software test question list, with a total of 90 items. In advance, according to the functions of various test question sheets, they are manually classified into 9 categories, forming a standard classification set, that is, these 90 test question sheets belong to 9 functions on average.

[0073] The data classification methods used for comparison include: the traditional LDA topic model (denoted as LDA-CF), the improved LDA topic model (denoted as RadarDCP-CF) proposed by the present invention and integrated into the airborne software requirements feature, and the mainstream classification algorithm support vector Machine SVM (denoted as SVM-CF).

[0074] The present invention uses the python language to realize the LDA topic model under the Windows7 operating system. The main parameters of the LDA topic model are set as follows: Gibbs sampling ...

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Abstract

The invention provides an intelligent classification method for airborne system software defects, and comprises the following steps: providing a word segmentation dictionary oriented to the aviation field to realize accurate word segmentation of airborne software; then, proposing an improved LDA topic model integrated with airborne software demand characteristics, acquiring implicit topics oriented by demands such as function names and interface names, and improving topic acquisition accuracy of the LDA model. On the basis of the obtained topic model, complex field characteristics and failure laws of the airborne software defect data can be well met, finally, accurate classification and prediction of the airborne software defect data are achieved, and then the efficiency and quality of airborne software test design work are improved.

Description

technical field [0001] The invention relates to a method for classifying software defects of an airborne system, belonging to the technical field of software. Background technique [0002] Software defect classification technology is an effective way to improve defect identification and prediction. Most software defect data are natural language texts, which are irregular and ambiguous. It is difficult for computers to process and classify software defect data effectively. However, if we only rely on manual methods to classify historical defect data and realize the reuse of the same type of software defects, the workload required is relatively large, and subject to human subjective factors, it is easy to cause omissions, and it is difficult to guarantee the efficiency of defect classification and quality. Therefore, using artificial intelligence technologies such as natural language processing to intelligently classify and reuse software defect data is one of the effective...

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Application Information

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IPC IPC(8): G06F16/36G06F16/35G06F40/242G06F40/247G06F40/284G06F40/289
CPCG06F16/353G06F16/374G06F40/284G06F40/289G06F40/242G06F40/247
Inventor 殷永峰李海峰刘畅宿庆冉王轶辰
Owner BEIHANG UNIV
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