Generating Test Inputs for Fault-Tree Analyzers using Imperative Predicates

This paper presents a case study on how Korat can be used in system testing, specifically in testing a large fault-tree analyzer developed for NASA. A fault-tree analyzer takes as input a fault tree that models how combinations of failures in the components of a system produce overall failures of the system. Testing a fault-tree analyzer requires generating fault trees. Korat is a previously developed testing tool that automates generation of structurally complex test inputs. Fault trees are structural in that they can be represented as graphs, and the nodes in the graphs need to satisfy certain complex constraints. Korat allows the user to express these constraints in widely used imperative programming languages such as Java. Previous research has shown how to test a fault-tree analyzer using another testing tool that requires the user to express constraints in a declarative language. This paper compares these two approaches. The results show that Korat generates a larger number of inputs but does not prune out non-equivalent inputs and thus can generate inputs that reveal errors in the system under test.