期刊
EMPIRICAL SOFTWARE ENGINEERING
卷 25, 期 3, 页码 2099-2136出版社
SPRINGER
DOI: 10.1007/s10664-020-09808-9
关键词
Software analytics; Data mining; Optimization; Evolutionary algorithms
资金
- NSF [1703487]
- EPSRC [EP/R006660/1, EP/R006660/2]
- ARC [DE160100850]
- EPSRC [EP/R006660/2, EP/R006660/1] Funding Source: UKRI
This paper claims that a new field of empirical software engineering research and practice is emerging: data mining using/used-by optimizers for empirical studies, or DUO. For example, data miners can generate models that are explored by optimizers. Also, optimizers can advise how to best adjust the control parameters of a data miner. This combined approach acts like an agent leaning over the shoulder of an analyst that advises ask this question next or ignore that problem, it is not relevant to your goals. Further, those agents can help us build better predictive models, where better can be either greater predictive accuracy or faster modeling time (which, in turn, enables the exploration of a wider range of options). We also caution that the era of papers that just use data miners is coming to an end. Results obtained from an unoptimized data miner can be quickly refuted, just by applying an optimizer to produce a different (and better performing) model. Our conclusion, hence, is that for software analytics it is possible, useful and necessary to combine data mining and optimization using DUO.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据