Warning classification method based on cost-sensitive neural network with oversampling operation

Abstract

The invention relates to an oversampling operation-based warning classification method for a cost-sensitive neural network. The method comprises the steps of S1, analyzing jar files with a series of versions in target software by using a FindBugs tool to obtain a static warning of the target software; S2, annotating the static warning obtained in the step S1; and S3, changing distribution of samples in a sample set in an oversampling manner by adopting the BP neural network to obtain a new sample set, training a classifier by using samples in the new sample set, performing prediction classification on all the samples in the sample set by using the classifier, and predicting all the samples in the sample set to be effective or false warnings . According to the method, an effective warning recall ratio is averagely increased by 44.07%, a relatively high and stable recall ratio can be quickly reached, and lower classification cost can be achieved in comparison with that in a conventional neural network method.

Description

technical field [0001] The invention relates to software static analysis, in particular to the classification of software static warnings, in particular to a warning classification method based on a cost-sensitive neural network based on oversampling operations. Background technique [0002] During the software development process, a lot of resources are consumed in finding and resolving bugs in software. Except for some large companies that use automated testing methods during the development process, in most small and medium-sized software companies or project teams, software testing is still in the manual processing stage. Its testing tasks are often heavy and inefficient. Smaller functional changes require regression testing of other functions. In the case of limited resources and time constraints, software testing work is usually not completed smoothly, resulting in many undiscovered errors remaining in the software system. The lack of testing methods has become a bot...

AI Technical Summary

Problems solved by technology

[0006] However, there are two types of problems in the warning classification process: the first type is the problem of unequal costs caused by misclassification, classifying a false positive warning as a valid warning (misclassification I), and classifying a valid warning as a false positive warning. Report a warning (misclassification II), and the resulting cost is not equal. It is different from the software defect prediction category. Misclassification II may cause software crashes, while misclassification I only requires developers to spend 5 minutes for review, that is, misclassification The cost of II will be much higher than that of misclassification I; the second type of problem is a class imbalance problem. As mentioned above, false positive warnings may account for the vast majority of overall warnings, and the experimental data of the present invention is also extremely unbalanced

The purpose of previous warning classification techniques is often to reduce the error rate of classification, while ignoring the different costs caused by the above-mentioned different classification errors and the imbalance of data

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