Search-Based Predictive Modelling for Software Engineering: How Far Have We Gone?

被引:4
作者
Sarro, Federica [1 ]
机构
[1] UCL, Dept Comp Sci, London, England
来源
SEARCH-BASED SOFTWARE ENGINEERING, SSBSE 2019 | 2019年 / 11664卷
关键词
Predictive analytics; Predictive modelling; Search-based software engineering; Machine learning; Software analytics;
D O I
10.1007/978-3-030-27455-9_1
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
In this keynote I introduce the use of Predictive Analytics for Software Engineering (SE) and then focus on the use of search-based heuristics to tackle long-standing SE prediction problems including (but not limited to) software development effort estimation and software defect prediction. I review recent research in Search-Based Predictive Modelling for SE in order to assess the maturity of the field and point out promising research directions. I conclude my keynote by discussing best practices for a rigorous and realistic empirical evaluation of search-based predictive models, a condicio sine qua non to facilitate the adoption of prediction models in software industry practices.
引用
收藏
页码:3 / 7
页数:5
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