A software defect prediction method and prediction system

Abstract

The invention discloses a software defect prediction method, which processes samples with and without class labels together, uses semi-supervised learning in Laplacian feature map (LE), improves the LE method, and at the same time, in order to avoid Map different types of samples into smaller low-dimensional neighborhoods, especially map defective samples into non-defective sample neighborhoods, and introduce cost-sensitive information when the LE algorithm calculates the distance between sample points to improve the performance of LE. Mapping accuracy, through which the discriminativeness of feature extraction can be effectively improved. The present invention also proposes a software defect prediction system. The present invention is applied to the NASA database, and the effectiveness of the proposed method is verified by experiments. Compared with other comparison methods, the classification performance is improved to a certain extent.

Description

technical field [0001] The invention relates to a software defect prediction method and a prediction system, belonging to the field of software engineering. Background technique [0002] Software defect prediction includes four links: data preprocessing, feature extraction, training prediction model, and identification. Feature extraction is one of the most basic problems in software defect prediction. For software defect prediction, extracting effective features is the primary task of recognition. [0003] Existing feature extraction methods can be divided into traditional dimensionality reduction methods and popular learning dimensionality reduction methods. Among them, the traditional dimensionality reduction methods include Principal Component Analysis (PCA) and Multidimensional Scaling Analysis (MDS). Popular learning dimension reduction methods: including isometric mapping (ISOMAP), Laplacian feature mapping (LE), locality-preserving projection (LPP), etc. [0004]...

AI Technical Summary

Problems solved by technology

[0024] For the traditional dimensionality reduction method, there are some deficiencies, such as Principal Component Analysis (PCA) method, its theory is perfect, and the calculation is effective, and it has a good dimensionality reduction effect on the data set whose internal structure is linear structure, but in the surface For linearly inseparable data, PCA cannot reflect the nonlinear nature of the data; and the multidimensional scaling algorithm (MDS) cannot handle sample data with nonlinear structures well.

For the popular learning dimension reduction method, although the isometric mapping method (ISOMAP) can reflect the intrinsic properties of nonlinear data, it is similar to MDS and is based on a global dimensionality reduction algorithm. This method does not take into account the local relationship of data samples. , and during dimensionality reduction, ISOMAP may produce an error of the "Elbow" phenomenon; although Laplacian eigenmap (LE) and local preservation projection (LPP) can process the local information of the sample, they do not take into account the category information of the sample

Images