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Roman Manevich

Researcher at Ben-Gurion University of the Negev

Publications -  32
Citations -  1069

Roman Manevich is an academic researcher from Ben-Gurion University of the Negev. The author has contributed to research in topics: Data structure & Abstract interpretation. The author has an hindex of 16, co-authored 32 publications receiving 1026 citations. Previous affiliations of Roman Manevich include University of California, Los Angeles & Tel Aviv University.

Papers
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Journal ArticleDOI

The tao of parallelism in algorithms

TL;DR: It is suggested that the operator formulation and tao-analysis of algorithms can be the foundation of a systematic approach to parallel programming.
Proceedings ArticleDOI

PSE: explaining program failures via postmortem static analysis

TL;DR: PSE (Postmortem Symbolic Evaluation), a static analysis algorithm that can be used by programmers to diagnose software failures, is described, which combines a novel dataflow analysis and memory alias analysis in a manner that allows for precise exploration of the program's behavior in polynomial time.
Book ChapterDOI

Predicate abstraction and canonical abstraction for singly-linked lists

TL;DR: This paper provides a new and rather precise family of abstractions for potentially cyclic singly-linked lists, and encoded this particular predicate abstraction and corresponding transformers in TVLA, and used this implementation to successfully verify safety properties of several list manipulating programs, including programs that were not previously verified using predicate abstraction or canonical abstraction.
Book ChapterDOI

Thread Quantification for Concurrent Shape Analysis

TL;DR: An instantiation of the technique with Canonical Abstraction as the base abstraction is implemented and used to successfully verify linearizability of data-structures in the presence of an unbounded number of threads.
Book ChapterDOI

Partially Disjunctive Heap Abstraction

TL;DR: One of the continuing challenges in abstract interpretation is the creation of abstractions that yield analyses that are both tractable and precise enough to prove interesting properties about real-world programs.