Building Theories from Multiple Evidence Sources

As emphasized in other chapters of this book, useful results in empirical software engineering require a variety of data to be collected through different studies - focusing on a single context or single metric rarely tells a useful story. But, in each study, the requirements of the local context are liable to impose different constraints on study design, the metrics to be collected, and other factors. Thus, even when all the studies focus on the same phenomenon (say, software quality), such studies can validly collect a number of different measures that are not at all compatible (say, number of defects required to be fixed during development, number of problem reports received from the customer, total amount of effort that needed to be spent on rework). Can anything be done to build a useful body of knowledge from these disparate pieces? This chapter addresses strategies that have been applied to date to draw conclusions from across such varied but valid data sets. Key approaches are compared and the data to which they are best suited are identified. Our analysis together with associated lessons learned provide decision support for readers interested in choosing and using such approaches to build up useful theories.