Floyd-Hoare Verifiers "Considered Harmful"

The Floyd-Hoare methodology completely dominates the field of program verification and has contributed much to our understanding of how programs might be analyzed. Useful but limited verifiers have been developed using Floyd-Hoare techniques. However, it has long been known that it is difficult to handle side effects on shared data structures within the Floyd-Hoare framework. Most examples of successful Floyd-Hoare axioms for assignment to complex data structures, similar statements have been used by London. This paper demonstrates an error in these formalizations and suggests a different style of verification.

Automatic Analysis of the Logical Structure of Programs

This report presents a method for viewing complex programs as built up out of simpler ones. The central idea is that typical programs are built up in a small number of stereotyped ways. The method is designed to make it easier for an automatic system to work with programs. It focuses on how the primitive operations performed by a program are combined together in order to produce the actions of the program as a whole. It does not address the issue of how complex data structures are built up from simpler ones, nor the relationships between data structures and the operations performed on them.

Learning from Incomplete Data

Real-world learning tasks often involve high-dimensional data sets with complex patterns of missing features. In this paper we review the problem of learning from incomplete data from two statistical perspectives---the likelihood-based and the Bayesian. The goal is two-fold: to place current neural network approaches to missing data within a statistical framework, and to describe a set of algorithms, derived from the likelihood-based framework, that handle clustering, classification, and function approximation from incomplete data in a principled and efficient manner. These algorithms are based on mixture modeling and make two distinct appeals to the Expectation-Maximization (EM) principle (Dempster, Laird, and Rubin 1977)---both for the estimation of mixture components and for coping with the missing data.