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 .

中文翻译：

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通过“DUO”进行更好的软件分析：优化器使用/使用的数据挖掘算法

本文声称，一个新的实证软件工程研究和实践领域正在出现：使用/使用优化器进行实证研究的数据挖掘，或 DUO。例如，数据挖掘者可以生成优化器探索的模型。此外，优化器可以建议如何最好地调整数据挖掘器的控制参数。这种组合方法就像一个代理靠在分析师的肩膀上，建议“接下来问这个问题”或“忽略那个问题，它与您的目标无关”。此外，这些代理可以帮助我们构建“更好”的预测模型，其中“更好”可以是更高的预测准确性或更快的建模时间（这反过来又可以探索更广泛的选项）。我们还警告说，仅使用数据挖掘器的论文时代即将结束。从未经优化的数据挖掘器获得的结果可以很快被驳斥，只需应用优化器来生成不同（且性能更好）的模型。因此，我们的结论是，对于软件分析，使用 DUO 结合数据挖掘和优化是可能、有用和必要的。