Testing by means of inductive program learning

Given a program P and a set of alternative programs //’, we generate a sequence of test cases that are adequate, in the sense that they distinguish the given program from all alternatives The, m(,thod is related to fault-based approaches to test case generation, but programs in P need not he s]mp]e mutations of P. The technique for generating an adequate test set is based on the inductive learning of programs from finite sets of input-output examples: given a partial test set. we generate inductively a program P ’ E P which is consistent with P on those input values; then we look for an input value that distinguishes P from P’, and we repeat the process until no program except P can be induced from the generated examples. We show that the obtained test set is adequate with respect to the alternatives belonging to P. The method IS made possible by a program induction procedure which has evolved from recent research in mnchine Iei]rnlng and inductive logic programming. An implemented version of the test case ~encration procedure is demonstrated on simple and more complex list-processing programs. and tb(, scalability of ’ the approach is discussed