Software defect prediction method based on migration learning

Abstract

The invention discloses a software defect prediction method based on migration learning. The method is characterized in that the idea of dimensionality reduction migration learning is utilized, probability distribution and conditional probability distribution among different project data samples are comprehensively considered, a new feature representation between a source project and a target project is established, difference between the two in the new space is minimized, a new classifier is trained, and migration learning is realized. According to the method, firstly, a distance measurementmode between different distributions is employed, maximum mean square difference is utilized to quantify distribution difference and conditional distribution difference between source data and targetdata, a model is acquired through metric minimization, training data and test data mapped by the model have almost the same probability distribution and conditional probability distribution, and secondly, the test data is classified through the traditional machine learning algorithm.

Description

technical field [0001] The invention relates to the technical field of software engineering, in particular to a software defect prediction method based on migration learning. Background technique [0002] In the past 30 years, software defect prediction has gradually become a significant research direction, dedicated to estimating how many remaining defects exist in the west facade of a software system. Software defect prediction can timely and accurately predict whether software modules contain defects in the early stage of system development, reasonably allocate test resources, and analyze defect modules in a targeted manner to improve product quality. In recent years, with the development of statistical learning and machine learning technology and its excellent prediction performance, software defect prediction methods based on statistical learning methods and machine learning methods have been gradually adopted by researchers and become the mainstream defect prediction t...

AI Technical Summary

Problems solved by technology

[0003] However, these classification techniques usually face a problem: when it is necessary to predict defects for new items, there are too few training samples, and it is difficult to train the correct model

The disadvantage of this method is that the dissimilar data discarded during the sample selection process will also cause the useful information contained in it to be discarded.

However, judging from the existing cross-engineering prediction performance, this method cannot produce better prediction results

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