Two-stage discriminant defect report severity prediction method based on spacy word vector

Abstract

The present invention provides a two-stage discriminant defect report severity prediction method based on Spacy word vectors. First, collect historical defect reports from the defect tracking system where the project is located, and extract description information summary attribute and severity attribute corresponding content to obtain defect report training The data set is then preprocessed and corresponding vectors are generated, and finally a two-stage discriminant process is performed to construct a severity prediction model. The beneficial effect of the present invention is: the present invention adopts naive Bayesian algorithm in the two-stage discrimination process, and this algorithm is easy to realize, and effect is good, can guarantee the accuracy rate of prediction model; On the one hand, it realizes the secondary utilization of the same batch of data, which is conducive to improving the performance of the model; on the other hand, the realization of two-stage discrimination can reduce the pressure of multi-classification Naive Bayesian prediction model when the classification of large categories is correct. Further improve the accuracy of the prediction model.

Description

technical field [0001] The invention relates to the technical field of software quality assurance, in particular to a two-stage discrimination defect report severity prediction method based on Spacy word vectors. Background technique [0002] Software defects and software development are inseparable like twins, which will affect software quality to a greater or lesser extent and require timely repair by technicians, and software defect repair operations run through the entire software development life cycle. Therefore, in order to ensure the quality of the developed software, how to improve the efficiency of software defect repair is a very critical issue. [0003] At present, in order to solve this problem, that is, in order to quickly locate and repair the defects in the software development process, many large-scale projects use the software defect report tracking system to record the defect information. The severity of software defect reports in the defect report tracki...

AI Technical Summary

Problems solved by technology

When further classifying defect reports, the traditional classification method represented by manual qualitative classification by developers has the disadvantages of being greatly affected by personal subjective factors, time-consuming, low efficiency, and high cost of time and energy. Therefore, it has become a challenge to find an automated alternative top priority

[0004] In recent years, in the field of software defect report severity prediction, most researchers have used classification or regression methods to predict the severity of software defect reports at one time. This method only uses the training set once to build a corresponding severity prediction model. , there is a result that the predictive model does not perform well

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