.png){kind=icon}
返回
Improving defect prediction with deep forest
.png){kind=icon}
PRE
AI
DOI：10.1016/j.infsof.2019.07.003.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Context: Software defect prediction is important to ensure the quality of software. Nowadays, many supervised learning techniques have been applied to identify defective instances (e.g., methods, classes, and modules). Objective: However, the performance of these supervised learning techniques are still far from satisfactory, and it will be important to design more advanced techniques to improve the performance of defect prediction models. Method: We propose a new deep forest model to build the defect prediction model (DPDF). This model can identify more important defect features by using a new cascade strategy, which transforms random forest classifiers into a layer-by-layer structure. This design takes full advantage of ensemble learning and deep learning. Results: We evaluate our approach on 25 open source projects from four public datasets (i.e., NASA, PROMISE, AEEEM and Relink). Experimental results show that our approach increases AUC value by 5% compared with the best traditional machine learning algorithms. Conclusion: The deep strategy in DPDF is effective for software defect prediction.
Keyword:
Software defect prediction
Deep forest
Cascade strategy
Empirical evaluation
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.3
论文数:
3.8K
被引数:
7.7K
机构
引用论文
A Systematic Literature Review on Fault Prediction Performance in Software Engineering软件工程中故障预测性能的系统文献综述
OAAI
.png){kind=icon}
OAAI分享收藏
TRTH-10. PEDIATRIC PHASE 1/1B STUDY OF ENTRECTINIB IN PATIENTS WITH PRIMARY BRAIN TUMORS, NEUROBLASTOMA, AND NTRK, ROS1, OR ALK FUSIONSTRTH-10.ENTRECTINIB在原发性脑肿瘤，神经母细胞瘤和NTRK，ROS1或ALK融合患者中的儿科1/1B期研究OAAI
OAAI分享收藏Enhancing developer recommendation with supplementary information via mining historical commits
PREAI
分享收藏
查看更多内容