Incremental software defect prediction method, system and equipment and storage medium

Abstract

The invention discloses an incremental software defect prediction method, system and device and a storage medium, and the method comprises the steps: obtaining a first training set, carrying out the preprocessing of the first training set, and obtaining a second training set; based on the second training set, constructing a cost-sensitive LightGBM model; a cost-sensitive LightGBM model is used as a base classifier to construct an incremental prediction model, and the total training sample number and the total defect sample number are calculated; calculating a classification threshold according to the total training sample number and the total defect sample number; predicting a to-be-predicted sample by using the incremental prediction model to obtain a final prediction value; and judging the category of the to-be-predicted sample based on the classification threshold and the final prediction value. According to the method, a new base classifier can be added on the basis of an original model, historical samples do not need to be repeatedly trained while new data are learned, the model calculation amount can be reduced, the storage space is saved, the training efficiency is improved, the model is continuously improved, and the accuracy of software defect prediction is improved.

Description

technical field

[0001] The present invention relates to the technical field of software defect prediction, in particular to an incremental software defect prediction method, system, device and storage medium. Background technique

[0002] In today's information age, the scale of software is becoming larger and larger, and the complexity of software structure is also increasing. In order to troubleshoot software defects and ensure the normal operation of the software, the cost of software testing should account for more than 50% of the cost of software development. However, in actual development, due to time, cost, and manpower constraints, the cost of software testing is often very low. Therefore, how to reasonably allocate software testing resources and how to conduct software testing with high quality and high efficiency has become a concern of researchers and software testing teams. Software defect prediction can divide software modules into defect-prone software module...

Images