00000cab a22000003a 4500 UP-99796217608835415 Buklod 20231007234835.0 m |o d | ta 090519s xx d | ||r ||||| || DENGII eng Perkins, Jeff H. Efficient incremental algorithms for dynamic detection of likely invariants. pp. 23-32 Dynamic detection of likely invariants is a program analysis that generalizes over observed values to hypothesize program properties. The reported program properties are a set of likely invariants over the program, also known as an operational abstraction. Operational abstractions are useful in testing, verification, bug detection, refactoring, comparing behavior, and many other tasks. Previous techniques for dynamic invariant detection scale poorly or report too few properties. Incremental algorithms are attractive because they process each observed value only once and thus scale well with data sizes. Previous incremental algorithms only checked and reported a small number of properties. This paper takes steps toward correcting this problem. The paper presents two new incremental algorithms for invariant detection and compares them analytically and experimentally to two existing algorithms. Furthermore, the paper presents four optimizations and shows how to implement them in the context of incremental algorithms. The result is more scalable invariant detection that does not sacrifice functionality. Theory of computation. Logics and meanings of programs. Specifying and Verifying and Reasoning about Programs. Invariants. Algorithms. Performance. Batch algorithm. Dynamic invariant detection. Incremental algorithm. Reversing optimizations. Software engineering notes. 29, 6 (2004). FO UPD DENG-II Article