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Thu 21 Jan 2016 15:35 - 16:00 at Grand Bay North - Track 1: Learning and verification Chair(s): David Monniaux

Formal specification is a vital ingredient to scalable verification of software systems. In the case of efficient implementations of concurrent objects like atomic registers, queues, and locks, symbolic formal representations of their abstract data types (ADTs) enable efficient modular reasoning, decoupling clients from implementations. Writing adequate formal specifications, however, is a complex task requiring rare expertise. In practice, programmers write reference implementations as informal specifications.

In this work we demonstrate that effective symbolic ADT representations can be automatically generated from the executions of reference implementations. Our approach exploits two key features of naturally-occurring ADTs: violations can be decomposed into a small set of representative patterns, and these patterns manifest in executions with few operations. By identifying certain algebraic properties of naturally-occurring ADTs, and exhaustively sampling executions up to a small number of operations, we generate concise symbolic ADT representations which are complete in practice, enabling the application of efficient symbolic verification algorithms without the burden of manual specification. Furthermore, the concise ADT violation patterns we generate are human-readable, and can serve as useful, formal documentation.

Thu 21 Jan

Displayed time zone: Guadalajara, Mexico City, Monterrey change

14:20 - 16:00
Track 1: Learning and verificationResearch Papers at Grand Bay North
Chair(s): David Monniaux CNRS, VERIMAG
14:20
25m
Talk
Combining Static Analysis with Probabilistic Models to Enable Market-Scale Analysis
Research Papers
Damien Octeau University of Wisconsin and Pennsylvania State University, Somesh Jha University of Wisconsin, Madison, Matthew Dering Pennsylvania State University, Patrick McDaniel Pennsylvania State University, Alexandre Bartel Technical University Darmstadt, Hongyu Li Rice University, Jacques Klein University of Luxembourg, Yves Le Traon University of Luxembourg
Media Attached
14:45
25m
Talk
Abstraction Refinement Guided by a Learnt Probabilistic Model
Research Papers
Radu Grigore University of Oxford, Hongseok Yang University of Oxford, UK
Media Attached
15:10
25m
Talk
Learning Invariants using Decision Trees and Implication Counterexamples
Research Papers
Pranav Garg University of Illinois at Urbana-Champaign, Daniel Neider University of Illinois at Urbana-Champaign, P. Madhusudan University of Illinois at Urbana-Champaign, Dan Roth University of Illinois at Urbana-Champaign
Media Attached
15:35
25m
Talk
Symbolic Abstract Data Type Inference
Research Papers
Michael Emmi IMDEA Software Institute, Constantin Enea LIAFA, Université Paris Diderot
Media Attached