After collecting data about software projects, and before making conclusions about those projects, there is a middle step in empirical software engineering where the data is interpreted. When the data is very large and/or is expressed in terms of some complex model of software projects, then interpretation is often accomplished, in part, via some automatic algorithm. For example, an increasing number of empirical studies base their conclusions on data mining algorithms (e.g. see 27menzies2013; menzim18r; bird2015art; menzies2013data; 2016tim) or model-intensive algorithms such as optimizers (e.g. see the recent section on Search-Based Software Engineering in the December 2016 issue of this journal Kessentini16).