Dynamically Discovering Likely Program Invariants to Support Program Evolution

Explicitly stated program invariants can help programmers by identifying program properties that must be preserved when modifying code. In practice, however, these invariants are usually implicit. An alternative to expecting programmers to fully annotate code with invariants is to automatically infer invariants from the program itself. This research focuses on dynamic techniques for discovering invariants from execution traces. This paper reports two results. First, it describes techniques for dynamically discovering invariants, along with an instrumenter and an inference engine that embody these techniques. Second, it reports on the application of the engine to two sets of target programs. In programs from Cries's work on program derivation, we rediscovered predefined invariants. In a C program lacking explicit invariants, we discovered invariants that assisted a software evolution task.