Budgeted testing through an algorithmic lens

Automated testing has been a focus of research for a long time. As such, we tend to think about this in a coverage centric manner. Testing budgets have also driven research such as prioritization and test selection, but as a secondary concern. As our systems get larger, are more dynamic, and impact more people with each change, we argue that we should switch from a coverage centric view to a budgeted testing centric view. Researchers in other fields have designed approximation algorithms for such budgeted scenarios and these are often simple to implement and run. In this paper we present an exemplar study on combinatorial interaction testing (CIT) to show that a budgeted greedy algorithm, when adapted to our problem for various budgets, does almost as well coverage-wise as a state of the art greedy CIT algorithm, better in some cases than a state of the art simulated annealing, and always improves over random. This suggests that we might benefit from switching our focus in large systems, from coverage to budgets.