Chasing errors using biasing automata

Abstract

Randomized testing is a lightweight approach for searching for bugs. It presents a tradeoff between the number of testing experiments performed and the probability to find errors. An important challenge in random testing is when the errors that we try to detect are scattered with very low probability among the different executions, forming a “rare event”. We suggest here the use of a “biasing automaton”, which observes the tested sequence and controls the distribution of the different choices of extending it. By the careful selection of a biasing automaton, we can increase the chance of errors to be found and consequently reduce the number of tests we need to perform. The biasing automaton is constructed through repeated testing of variants of the system under test. We show how to construct biasing automata based on genetic programming.