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A Software Defect Prediction Method Based on Feature Extraction and Stacking Integrated Learning

A software defect prediction and feature extraction technology, applied in software testing/debugging, computer components, error detection/correction, etc., to achieve good results

Active Publication Date: 2022-05-20
SOUTHWEST UNIVERSITY FOR NATIONALITIES
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Problems solved by technology

[0004] In order to overcome the shortcomings of existing defect prediction methods, the present invention proposes a software defect prediction method based on feature extraction and Stacking integrated learning, thereby solving the aforementioned problems in the prior art

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  • A Software Defect Prediction Method Based on Feature Extraction and Stacking Integrated Learning
  • A Software Defect Prediction Method Based on Feature Extraction and Stacking Integrated Learning
  • A Software Defect Prediction Method Based on Feature Extraction and Stacking Integrated Learning

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Embodiment Construction

[0019] Now in conjunction with embodiment, accompanying drawing, the present invention will be further described:

[0020] The present invention proposes a software defect prediction method (KSSDP) based on feature extraction and Stacking integrated learning, and the flow chart of the KSSDP integrated prediction model is shown in figure 1 , the technical solutions adopted to solve its technical problems include the following:

[0021] 1. Feature extraction on the original data set

[0022] The original data points in the low-dimensional feature space are mapped to the high-dimensional feature space by using the nonlinear mapping kernel function, so as to extract representative features and characterize the complex defect data structure. Its core principles are as follows:

[0023] Suppose x is mapped into u by a corresponding function ρ, which is defined as follows:

[0024] u=ρ(x) (1)

[0025] The kernel function maps the data to the corresponding N-dimensional feature sp...

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Abstract

The invention discloses a software defect prediction method based on feature extraction and Stacking integrated learning, including: (1) using kernel principal component analysis to perform feature extraction on an original data set to obtain a defect data set DS' after dimensionality reduction; (2) ) Utilize the collaborative filtering algorithm proposed by the present invention to recommend an applicable sampling method for new software defect data, use the recommended sampling algorithm to perform unbalanced processing on the defect data set DS′, and obtain the unbalanced processed defect data set DS″; (3) Use the K-Means algorithm to cluster the defect data set DS″, and remove outliers that deviate from the mainstream category to obtain the defect data set DS″’; (4) Build a software defect prediction model based on Stacking integrated learning, Select an appropriate classifier for the base learner of the first layer and the meta-learner of the second layer, and construct a software defect prediction model with good performance; (5) use the integrated The model is compared with the base model and the mainstream integrated model to verify the performance of the integrated prediction model proposed by the present invention. The research results show that the performance of the KSSDP ensemble prediction model proposed by the present invention is better than that of the base model and mainstream ensemble model.

Description

technical field [0001] The invention relates to the field of software defects, in particular to a software defect prediction method based on feature extraction and Stacking integrated learning. Background technique [0002] Open source software is one of the main trends in the future development of the software industry, how to ensure its quality has always been a concern and crucial issue in the industry. The openness and community-based sharing of open source software make the source code often contain many loopholes, resulting in a substantial increase in the cost of defect handling and hindering the application and promotion of open source software. Therefore, identifying and controlling defect-introducing factors in the early stage of software development has very important practical significance for formulating effective defect prevention measures, reducing the rate of defect introduction and ensuring software quality. The current mainstream defect prediction technolo...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06F11/36
CPCG06F11/3672G06F18/22G06F18/23213G06F18/2135G06F18/24Y02P90/30
Inventor 崔梦天吴克奇李卫榜王琳姜玥罗洪
Owner SOUTHWEST UNIVERSITY FOR NATIONALITIES
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