From program verification to program synthesis

Srivastava, Saurabh; Gulwani, Sumit; Foster, Jeffrey S.

doi:10.1145/1707801.1706337

Cited by 88 publications

(64 citation statements)

References 35 publications

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“…In particular, we target the translation step from high-level constraints to SAT in bit-vector solvers. Bit-vector solvers are widely used in [18,29,45,50] because bit-vectors can be used to faithfully represent the full range of machine arithmetic and the translation from the input high-level constraints to SAT is a very crucial step in these solvers. Internally, these high-level constraints are stored as directed acyclic graphs (DAG) which we call formulas.…”

Section: Introductionmentioning

confidence: 99%

Synthesis of Domain Specific CNF Encoders for Bit-Vector Solvers

Inala

Singh

Solar-Lezama

2016

Theory and Applications of Satisfiability Testing – SAT 2016

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SMT solvers are at the heart of a number of software engineering tools. These SMT solvers use a SAT solver as the back-end and convert the high-level constraints given by the user down to low-level boolean formulas that can be efficiently mapped to CNF clauses and fed into a SAT solver. Current SMT solvers are designed to be general purpose solvers that are suited to a wide range of problems. However, SAT solvers are very non-deterministic and hence, it is difficult to optimize a general purpose solver across all different problems. In this thesis, we propose a system that can automatically generate parts of SMT solvers in a way that is tailored to particular problem domains. In particular, we target the translation from high-level constraints to CNF clauses which is one of the crucial parts of all SMT solvers. We achieve this goal by using a combination of program synthesis and machine learning techniques. We use a program synthesis tool called Sketch to generate optimal encoding rules for this translation and then use auto-tuning to only select the subset of these encodings that actually improve the performance for a particular class of problems.Using this technique, the thesis shows that we can improve upon the basic encoding strategy used by CVC4 (a state of the art SMT solver). We can automatically generate variants of the solver tailored to different domains of problems represented in the bit-vector benchmark suite from the SMT competition 2015.

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Section: Introductionmentioning

confidence: 99%

Synthesis of Domain Specific CNF Encoders for Bit-Vector Solvers

Inala

Singh

Solar-Lezama

2016

Theory and Applications of Satisfiability Testing – SAT 2016

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“…; if (x > c) { y := x + 1; } else { y := 10; } return y } while satisfying the safety invariant B : (c − 10 ≤ y ≤ c + 10), and a quantitative specification that states that the return value of the procedure should be as low as possible. This is an example of a verified parameter synthesis problem [25,30,32,33]. The programmer has provided an implementation with missing numerical parameters -also known as a "sketch" [30]-and the synthesis problem is to find values for these parameters such that the resulting program satisfies the combined Boolean and quantitative specification.…”

Section: Introductionmentioning

confidence: 99%

Bridging boolean and quantitative synthesis using smoothed proof search

Chaudhuri

Clochard

Solar-Lezama

2014

Proceedings of the 41st ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages

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We present a new technique for parameter synthesis under boolean and quantitative objectives. The input to the technique is a "sketch" -a program with missing numerical parameters -and a probabilistic assumption about the program's inputs. The goal is to automatically synthesize values for the parameters such that the resulting program satisfies: (1) a boolean specification, which states that the program must meet certain assertions, and (2) a quantitative specification, which assigns a real valued rating to every program and which the synthesizer is expected to optimize.Our method -called smoothed proof search -reduces this task to a sequence of unconstrained smooth optimization problems that are then solved numerically. By iteratively solving these problems, we obtain parameter values that get closer and closer to meeting the boolean specification; at the limit, we obtain values that provably meet the specification. The approximations are computed using a new notion of smoothing for program abstractions, where an abstract transformer is approximated by a function that is continuous according to a metric over abstract states.We present a prototype implementation of our synthesis procedure, and experimental results on two benchmarks from the embedded control domain. The experiments demonstrate the benefits of smoothed proof search over an approach that does not meet the boolean and quantitative synthesis goals simultaneously.

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“…Over the last few years, constraint-based approaches to synthesis based on sketches or templates have become quite popular [11,19,26,28,29]. In these approaches, the user specifies her intent with a template of the desired solution leaving parts of the code unspecified (a sketch), and the synthesizer finds the unknown code fragments such that the completed template conforms to a given set of behavioral constraints (the spec).…”

Section: Introductionmentioning

confidence: 99%

Modular Synthesis of Sketches Using Models

Singh

et al. 2014

Lecture Notes in Computer Science

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Abstract. One problem with the constraint-based approaches to synthesis that have become popular over the last few years is that they only scale to relatively small routines, on the order of a few dozen lines of code. This paper presents a mechanism for modular reasoning that allows us to break larger synthesis problems into small manageable pieces. The approach builds on previous work in the verification community of using high-level specifications and partially interpreted functions (we call them models) in place of more complex pieces of code in order to make the analysis modular. The main contribution of this paper is to show how to combine these techniques with the counterexample guided synthesis approaches used to efficiently solve synthesis problems. Specifically, we show two new algorithms; one to efficiently synthesize functions that use models, and another one to synthesize functions while ensuring that the behavior of the resulting function will be in the set of behaviors allowed by the model. We have implemented our approach on top of the open-source Sketch synthesis system, and we demonstrate its effectiveness on several Sketch benchmark problems.

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From program verification to program synthesis

Cited by 88 publications

References 35 publications

Synthesis of Domain Specific CNF Encoders for Bit-Vector Solvers

Synthesis of Domain Specific CNF Encoders for Bit-Vector Solvers

Bridging boolean and quantitative synthesis using smoothed proof search

Modular Synthesis of Sketches Using Models

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