Lessons Learned from Software Analytics in Practice

In this chapter, we share our experience and views on software data analytics in practice with a review of our previous work. In more than 10 years of joint research projects with industry, we have encountered similar data analytics patterns in diverse organizations and in different problem cases. We discuss these patterns following a "software analytics" framework: problem identification, data collection, descriptive statistics, and decision making. In the discussion, our arguments and concepts are built around our experiences of the research process in six different industry research projects in four different organizations.Methods: Spearman rank correlation, Pearson correlation, Kolmogorov-Smirnov test, chi-square goodness-of-fit test, t test, Mann-Whitney U test, Kruskal-Wallis analysis of variance, k-nearest neighbor, linear regression, logistic regression, naïve Bayes, neural networks, decision trees, ensembles, nearest-neighbor sampling, feature selection, normalization.