This paper is a study of persistence in data structures. Ordinary data structures are ephemeral in the sense that a change to the structure destroys the old version, leaving only the new version available for use. In contrast, a persistent structure allows access to any version, old or new, at any time. We develop simple, systematic, and effiient techniques for making linked data structures persistent. We use our techniques to devise persistent forms of binary search trees with logarithmic access, insertion, and deletion times and O(1) space bounds for insertion and deletion.

This paper concerns static-analysis algorithms for analyzing x86 executables.

Higher Order Attribute Grammars (HAGs) are an extension of normal attribute grammars in the sense that the distinction between the domain of parse-trees and the domain of attributes has disappeared: parse trees may be computed in attributes and grafted to the parse tree at various places. As a result semantic functions may be described by attribute evaluation.

this article, we present a general framework for developing demand-driven interprocedural data flow analyzers and report our experience in evaluating the performance of this approach. A demand for data flow information is modeled as a set of queries. The framework includes a generic demand-driven algorithm that determines the response to a query by iteratively applying a system of query propagation rules. The propagation rules yield precise responses for the class of distributive finite data flow problems. We also describe a two-phase framework variation to accurately handle nondistributive problems. A performance evaluation of our demand-driven approach is presented for two data flow problems, namely, reaching-definitions and copy constant propagation. Our experiments show that demand-driven analysis performs well in practice, reducing both time and space requirements when compared with exhaustive analysis.

We introduce the notion of persistent authenticated dictionaries, that is, dictionaries where the user can make queries of the type "was element e in set S at time t?" and get authenticated answers. Applications include credential and certificate validation checking in the past (as in digital signatures for electronic contracts), digital receipts, and electronic tickets. We present two data structures that can efficiently support an infrastructure for persistent authenticated dictionaries, and we compare their performance.

Abstract not found

Over the last seven years, we have developed static-analysis methods to recover a good approximation to the variables and dynamically-allocated memory objects of a stripped executable, and to track the flow of values through them. The paper presents the algorithms that we developed, explains how they are used to recover intermediate representations (IRs) from executables that are similar to the IRs that would be available if one started from source code, and describes their application in the context of program understanding and automated bug hunting. Unlike algorithms for analyzing executables that existed prior to our work, the ones presented in this paper provide useful information about memory accesses, even in the absence of debugging information. The ideas described in the paper are incorporated in a tool for analyzing Intel x86 executables, called CodeSurfer/x86. CodeSurfer/x86 builds a system dependence graph for the program, and provides a GUI for exploring the graph by (i) navigating its edges, and (ii) invoking operations, such as forward slicing, backward slicing, and chopping, to discover how parts of the program can impact other parts. To assess the usefulness of the IRs recovered by CodeSurfer/x86 in the context of automated bug hunting, we built a tool on top of CodeSurfer/x86, called Device-Driver Analyzer for x86

A systematic approach is given for deriving incremental programs that exploit caching. The cache-and-prune method presented in the article consists of three stages: (I) the original program is extended to cache the results of all its intermediate subcomputations as well as the nal result, (II) the extended program is incrementalized so that computation on a new input can use all intermediate results on an old input, and (III) unused results cached by the extended program and maintained by the incremental program are pruned away, l e a ving a pruned extended program that caches only useful intermediate results and a pruned incremental program that uses and maintains only the useful results. All three stages utilize static analyses and semantics-preserving transformations. Stages I and III are simple, clean, and fully automatable. The overall method has a kind of optimality with respect to the techniques used in Stage II. The method can be applied straightforwardly to provide a systematic approach to program improvement via caching.

One-way dataflow constraints have gained popularity in many types of interactive systems because of their simplicity, efficiency, and manageability. Although it is widely acknowledged that multi-way dataflow constraints could make it easier to specify certain relationships in these applications, concerns about their predictability and efficiency have impeded their acceptance. Constraint hierarchies have been developed to address the predictability problem and incremental algorithms have been developed to address the efficiency problem. However, existing incremental algorithms for satisfying constraint hierarchies encounter two difficulties: (1) they are incapable of guaranteeing an acyclic solution if a constraint hierarchy has one or more cyclic solutions, and (2) they require worst-case exponential time to satisfy systems of multi-output constraints. This paper surmounts these difficulties by presenting an 2 incremental algorithm called QuickPlan that satisfies in worst case O(N ) ...

A systematic approach i s g i v en for deriving incremental programs from non-incremental programs written in a standard functional programming language. We exploit a number of program analysis and transformation techniques and domain-speci c knowledge, centered around e ective utilization of caching, in order to provide a degree of incrementality not otherwise achievable by a generic incremental evaluator. 1