Software defect prediction method based on genetic algorithm and random forest

Abstract

The invention discloses a software defect prediction method based on a genetic algorithm and a random forest. The software defect prediction method comprises the following steps of carrying out data preprocessing on each subset of a software defect data set; performing feature selection based on the genetic algorithm and a random forest algorithm; constructing a random forest classifier; and carrying out the software defect prediction, training the random forest classifier by using the processed software defect data set, obtaining the random forest classifier with a better classification effect through a multi-test experiment, and then inputting the processed software defect test set into the trained classifier to finally obtain a classification result of the test set. The method is well suitable for the software defect data sets with difference and class imbalance; the genetic algorithm and the random forest algorithm are combined for feature selection, so that a very good dimension reduction effect is achieved. By using an integrated algorithm based on a decision tree to independently learn and make predictions, and combining the prediction results, a final prediction result is obtained.

Description

Technical field [0001] The present invention relates to the field of computers, in particular to a software defect prediction method based on genetic algorithm and random forest. Background technique [0002] Software defects are the source of software failure and an important factor that affects software reliability. It is particularly important in the field of software engineering to predict the defects in the software as early as possible so as to allocate test verification resources reasonably and ensure software quality. Software defect prediction technology has become an important research direction in the field of software engineering, which is mainly divided into static prediction and dynamic prediction. Static prediction is based on defect-related measurement data to predict the number and distribution of defects; dynamic prediction is to predict the distribution of failures over time. Among them, static prediction technology is the more common technology used in early ...

AI Technical Summary

Problems solved by technology

[0005] Due to the large differences in software defect datasets and the extremely unbalanced categories, the performance of machine learning algorithms is not very good under different evaluation indicators. At the same time, many existing technologies have not dealt with the problem of category imbalance well. , so that the performance of software defect prediction is not good

Images