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Software Defect Prediction Based on Ensemble Learning
- Source :
- DSIT
- Publication Year :
- 2019
- Publisher :
- ACM, 2019.
-
Abstract
- Software defect prediction is one of the important ways to guarantee the quality of software systems. Combining various algorithms in machine learning to predict software defects has become a hot topic in the current study. The paper uses the datasets of MDP as the experimental research objects and takes ensemble learning as research focus to construct software defect prediction model. With experimenting five different types of ensemble algorithms and analyzing the features and procedures, this paper discusses the best ensemble algorithm which is Random Forest through experimental comparison. Then we utilize the SMOTE over-sampling and Resample methods to improve the quality of datasets to build a complete new software defect prediction model. Therefore, the results show that the model can improve defect classification performance effectively.
- Subjects :
- Computer science
business.industry
media_common.quotation_subject
020207 software engineering
02 engineering and technology
Construct (python library)
Machine learning
computer.software_genre
Ensemble learning
Random forest
Software
Software bug
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Quality (business)
Software system
Artificial intelligence
business
Focus (optics)
computer
media_common
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Proceedings of the 2019 2nd International Conference on Data Science and Information Technology
- Accession number :
- edsair.doi...........71df82eec84062fc9091e2c8ecceeb3a