Cross-project software defect prediction method based on supervised expression learning

Abstract

The invention discloses a cross-project software defect prediction method for supervised expression learning. The method comprises the following steps: (1) selecting a defect data set, and preprocessing defect data; (2) training a migration auto-encoder in an unsupervised pre-training mode, wherein the migration auto-encoder comprises a feature coding layer and a label coding layer; (3) with the help of a migration cross validation method, selecting a sample closest to the hidden layer feature distribution of the target project sample from all sample hidden layer feature representations of thesource project as a validation set, and taking the rest as a training set; (4) performing oversampling processing on the training set sample; (5) finely adjusting the migration auto-encoder, and selecting a model hyper-parameter and an early stop strategy; and (6) inputting the preprocessed data of the target project into a migration auto-encoder, and obtaining a final prediction result through the output of a label encoding layer. According to the method, the label information of the source project sample is introduced into the feature representation learning process, so that the predictionperformance of the cross-project software defect prediction model is improved.

Description

technical field [0001] The invention belongs to the technical field of software defect prediction for software engineering applications, and in particular relates to a cross-project software defect prediction method based on supervised representation learning. Background technique [0002] Software defect prediction technology learns from historical defect data and builds a prediction model to predict possible defects in current software projects. It can help testers quickly find defects and greatly improve the efficiency of software testing, so it has become a research hotspot in the field of software engineering. [0003] The usual practice of software defect prediction is to first extract various features from software code, such as Halstead metric, McCabe metric, CK metric, MOOD metric, code change metric and other object-oriented metric, express all code segments as feature vectors, and Mark according to whether there are actual defects, and then input these feature ve...

AI Technical Summary

Problems solved by technology

But the divide and conquer method itself has a problem: when solving subproblems step by step, although the optimal solution can be obtained on the subproblem, the optimal solution on the subproblem does not mean that the optimal solution to the global problem can be obtained

The features learned in the early stage may not be suitable for the later training classifier, which may affect the actual prediction ability of the final software defect prediction model

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