1,371 research outputs found
Abstract Learning Frameworks for Synthesis
We develop abstract learning frameworks (ALFs) for synthesis that embody the
principles of CEGIS (counter-example based inductive synthesis) strategies that
have become widely applicable in recent years. Our framework defines a general
abstract framework of iterative learning, based on a hypothesis space that
captures the synthesized objects, a sample space that forms the space on which
induction is performed, and a concept space that abstractly defines the
semantics of the learning process. We show that a variety of synthesis
algorithms in current literature can be embedded in this general framework.
While studying these embeddings, we also generalize some of the synthesis
problems these instances are of, resulting in new ways of looking at synthesis
problems using learning. We also investigate convergence issues for the general
framework, and exhibit three recipes for convergence in finite time. The first
two recipes generalize current techniques for convergence used by existing
synthesis engines. The third technique is a more involved technique of which we
know of no existing instantiation, and we instantiate it to concrete synthesis
problems
Invariant Synthesis for Incomplete Verification Engines
We propose a framework for synthesizing inductive invariants for incomplete
verification engines, which soundly reduce logical problems in undecidable
theories to decidable theories. Our framework is based on the counter-example
guided inductive synthesis principle (CEGIS) and allows verification engines to
communicate non-provability information to guide invariant synthesis. We show
precisely how the verification engine can compute such non-provability
information and how to build effective learning algorithms when invariants are
expressed as Boolean combinations of a fixed set of predicates. Moreover, we
evaluate our framework in two verification settings, one in which verification
engines need to handle quantified formulas and one in which verification
engines have to reason about heap properties expressed in an expressive but
undecidable separation logic. Our experiments show that our invariant synthesis
framework based on non-provability information can both effectively synthesize
inductive invariants and adequately strengthen contracts across a large suite
of programs
Synthesizing Modular Invariants for Synchronous Code
In this paper, we explore different techniques to synthesize modular
invariants for synchronous code encoded as Horn clauses. Modular invariants are
a set of formulas that characterizes the validity of predicates. They are very
useful for different aspects of analysis, synthesis, testing and program
transformation. We describe two techniques to generate modular invariants for
code written in the synchronous dataflow language Lustre. The first technique
directly encodes the synchronous code in a modular fashion. While in the second
technique, we synthesize modular invariants starting from a monolithic
invariant. Both techniques, take advantage of analysis techniques based on
property-directed reachability. We also describe a technique to minimize the
synthesized invariants.Comment: In Proceedings HCVS 2014, arXiv:1412.082
Verification and Synthesis of Symmetric Uni-Rings for Leads-To Properties
This paper investigates the verification and synthesis of parameterized
protocols that satisfy leadsto properties on symmetric
unidirectional rings (a.k.a. uni-rings) of deterministic and constant-space
processes under no fairness and interleaving semantics, where and are
global state predicates. First, we show that verifying for
parameterized protocols on symmetric uni-rings is undecidable, even for
deterministic and constant-space processes, and conjunctive state predicates.
Then, we show that surprisingly synthesizing symmetric uni-ring protocols that
satisfy is actually decidable. We identify necessary and
sufficient conditions for the decidability of synthesis based on which we
devise a sound and complete polynomial-time algorithm that takes the predicates
and , and automatically generates a parameterized protocol that
satisfies for unbounded (but finite) ring sizes. Moreover, we
present some decidability results for cases where leadsto is required from
multiple distinct predicates to different predicates. To demonstrate
the practicality of our synthesis method, we synthesize some parameterized
protocols, including agreement and parity protocols
Automatically Leveraging MapReduce Frameworks for Data-Intensive Applications
MapReduce is a popular programming paradigm for developing large-scale,
data-intensive computation. Many frameworks that implement this paradigm have
recently been developed. To leverage these frameworks, however, developers must
become familiar with their APIs and rewrite existing code. Casper is a new tool
that automatically translates sequential Java programs into the MapReduce
paradigm. Casper identifies potential code fragments to rewrite and translates
them in two steps: (1) Casper uses program synthesis to search for a program
summary (i.e., a functional specification) of each code fragment. The summary
is expressed using a high-level intermediate language resembling the MapReduce
paradigm and verified to be semantically equivalent to the original using a
theorem prover. (2) Casper generates executable code from the summary, using
either the Hadoop, Spark, or Flink API. We evaluated Casper by automatically
converting real-world, sequential Java benchmarks to MapReduce. The resulting
benchmarks perform up to 48.2x faster compared to the original.Comment: 12 pages, additional 4 pages of references and appendi
Invariant Synthesis for Incomplete Verification Engines
We propose a framework for synthesizing inductive invariants for incomplete verification engines, which soundly reduce logical problems in undecidable theories to decidable theories. Our framework is based on the counter-example guided inductive synthesis principle (CEGIS) and allows verification engines to communicate non-provability information to guide invariant synthesis. We show precisely how the verification engine can compute such non-provability information and how to build effective learning algorithms when invariants are expressed as Boolean combinations of a fixed set of predicates. Moreover, we evaluate our framework in two verification settings, one in which verification engines need to handle quantified formulas and one in which verification engines have to reason about heap properties expressed in an expressive but undecidable separation logic. Our experiments show that our invariant synthesis framework based on non-provability information can both effectively synthesize inductive invariants and adequately strengthen contracts across a large suite of programs
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