Exact and approximate methods for proving unrealizability of syntax-guided synthesis problems

Hu, Qinheping; Cyphert, John; D’Antoni, Loris; Reps, Thomas

doi:10.1145/3385412.3385979

Cited by 20 publications

(16 citation statements)

References 13 publications

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“…The output from Z3 is either UNSAT, which means the problem is unrealizable, or a proof for Realizable from Equation (8) using the inference rules from the SemGuS problem: in this case, MESSY can extract a solution to the SemGuS problem from the proof. 7 We note that the capability of MESSY to synthesize programs also allows it to perform CEGIS for both program synthesis and unrealizability, which is unsupported in previous work on proving unrealizability [Hu et al 2019[Hu et al , 2020. We report here that Z3 itself varied in performance depending on whether particular internal flags were enabled.…”

Section: Implemention and Optimizationsmentioning

confidence: 83%

“…As shown in prior work [Solar-Lezama 2013], the CEGIS algorithm is often powerful enough for program synthesis, where a term synthesized for the given examples generalizes to the entire space of possible inputs. Prior work on unrealizability [Hu et al 2019[Hu et al , 2020 also shows that CEGIS is often powerful enough to prove that a synthesis problem is unrealizableÐi.e., the problem does not admit a solution even when only a finite number of examples are considered.…”

Section: Solving Semgus Problems With Counterexample-guided Inductivementioning

confidence: 99%

“…The first set consists of 132 unrealizable variants of the 60 LIA (Linear Integer Arithmetic) benchmarks from the LIA SyGuS competition track. These benchmarks were generated by Hu et al [Hu et al 2019] and have been used as benchmarks for unrealizability in previous work [Hu et al 2019[Hu et al , 2020. These benchmarks originate from SyGuS problems where the goal is to prove that a synthesized solution is optimal with respect to some metric [Hu and D'Antoni 2018] (e.g., minimizing the number of If-Then-Else operators).…”

Section: Benchmarksmentioning

confidence: 99%

“…We note that the front-end processing step, to encode a SemGuS problem into CHCs, took less than 3 minutes for all of our benchmarks and configurations combined; the 10-minute timeout was separate from the front-end processing step, and devoted entirely to CHC solving. Table 1 summarizes the numbers of solved benchmarks for various configurations of MESSY we tested, as well as comparisons for Nay [Hu et al 2020], ESolver [Alur et al 2017b], CVC4 [Reynolds et al 2015], SIMPL [So and Oh 2017], and AlphaRegex [Lee et al 2016].…”

Section: Benchmarksmentioning

confidence: 99%

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Semantics-guided synthesis

Kim

D’Antoni

et al. 2021

Proc. ACM Program. Lang.

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This paper develops a new framework for program synthesis, called semantics-guided synthesis (SemGuS), that allows a user to provide both the syntax and the semantics for the constructs in the language. SemGuS accepts a recursively defined big-step semantics, which allows it, for example, to be used to specify and solve synthesis problems over an imperative programming language that may contain loops with unbounded behavior. The customizable nature of SemGuS also allows synthesis problems to be defined over a non-standard semantics, such as an abstract semantics. In addition to the SemGuS framework, we develop an algorithm for solving SemGuS problems that is capable of both synthesizing programs and proving unrealizability, by encoding a SemGuS problem as a proof search over Constrained Horn Clauses: in particular, our approach is the first that we are aware of that can prove unrealizabilty for synthesis problems that involve imperative programs with unbounded loops, over an infinite syntactic search space. We implemented the technique in a tool called MESSY, and applied it to SyGuS problems (i.e., over expressions), synthesis problems over an imperative programming language, and synthesis problems over regular expressions.

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Section: Implemention and Optimizationsmentioning

confidence: 83%

Section: Solving Semgus Problems With Counterexample-guided Inductivementioning

confidence: 99%

Section: Benchmarksmentioning

confidence: 99%

Section: Benchmarksmentioning

confidence: 99%

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Semantics-guided synthesis

Kim

D’Antoni

et al. 2021

Proc. ACM Program. Lang.

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“…In this section, we introduce preliminary concepts including syntax-guided synthesis over a finite set of examples, the top-down deductive synthesis strategy and version-space algebra (some notations are borrowed from [Hu et al 2020] and ).…”

Section: Preliminariesmentioning

confidence: 99%

Combining the top-down propagation and bottom-up enumeration for inductive program synthesis

Lee

2021

Proc. ACM Program. Lang.

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We present an effective method for scalable and general-purpose inductive program synthesis. There have been two main approaches for inductive synthesis: enumerative search, which repeatedly enumerates possible candidate programs, and the top-down propagation (TDP), which recursively decomposes a given large synthesis problem into smaller subproblems. Enumerative search is generally applicable but limited in scalability, and the TDP is efficient but only works for special grammars or applications. In this paper, we synergistically combine the two approaches. We generate small program subexpressions via enumerative search and put them together into the desired program by using the TDP. Enumerative search enables to bring the power of TDP into arbitrary grammars, and the TDP helps to overcome the limited scalability of enumerative search. We apply our approach to a standard formulation, syntax-guided synthesis (SyGuS), thereby supporting a broad class of inductive synthesis problems. We have implemented our approach in a tool called Duet and evaluate it on SyGuS benchmark problems from various domains. We show that Duet achieves significant performance gains over existing general-purpose as well as domain-specific synthesizers.

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Approximate Bit Dependency Analysis to Identify Program Synthesis Problems as Infeasible

Kamp

Philippsen

2021

Lecture Notes in Computer Science

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Bit-vector-based program synthesis is an important building block of state-of-the-art techniques in computer programming. Some of these techniques do not only rely on a synthesizer's ability to return an appropriate program if it exists but also require a synthesizer to detect if there is no such program at all in the entire search space (i.e., the problem is infeasible), which is a computationally demanding task.In this paper, we propose an approach to quickly identify some synthesis problems as infeasible. We observe that a specification function encodes dependencies between input and output bits that a correct program must satisfy. To exploit this fact, we present approximate analyses of essential bits and use them in two novel algorithms to check if a synthesis problem is infeasible. Our experiments show that adding our technique to applications of bit vector synthesis can save up to 33% of their time.

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Exact and approximate methods for proving unrealizability of syntax-guided synthesis problems

Cited by 20 publications

References 13 publications

Semantics-guided synthesis

Semantics-guided synthesis

Combining the top-down propagation and bottom-up enumeration for inductive program synthesis

Approximate Bit Dependency Analysis to Identify Program Synthesis Problems as Infeasible

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