.png){kind=icon}
返回
Machine Learning for Software Engineering: A Tertiary Study
.png){kind=icon}
OA
AI
DOI：10.1145/3572905.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Machine learning (ML) techniques increase the effectiveness of software engineering (SE) lifecycle activities. We systematically collected, quality-assessed, summarized, and categorized 83 reviews in ML for SE published between 2009 and 2022, covering 6,117 primary studies. The SE areas most tackled with ML are software quality and testing, while human-centered areas appear more challenging for ML. We propose a number of ML for SE research challenges and actions, including conducting further empirical validation and industrial studies on ML, reconsidering deficient SE methods, documenting and automating data collection and pipeline processes, reexamining how industrial practitioners distribute their proprietary data, and implementing incremental ML approaches.
Keyword:
Tertiary study
machine learning
software engineering
systematic literature
review
期刊
IF:
28
论文数:
2.5K
被引数:
3.5W
机构
暂无机构信息
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
OAAI分享收藏Taxonomies in software engineering: A Systematic mapping study and a revised taxonomy development method软件工程中的分类法: 系统的映射研究和修订的分类法开发方法OAAI
OAAI分享收藏Coalition game for user association and bandwidth allocation in ultra-dense mmWave networks超密集毫米波网络中用户关联和带宽分配的联盟博弈
PREAI
分享收藏Novel metrics to quantify the impacts of frequency support provision methods by wind power量化风电频率支持提供方法影响的新指标OAAI
OAAI分享收藏A Systematic Literature Review and Quality Analysis of Javascript Malware Detection.png)
IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏A Systematic Review on Test Suite Reduction: Approaches, Experiment's Quality Evaluation, and GuidelinesIEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏
查看更多内容