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

Lee, Woosuk

doi:10.1145/3434335

Cited by 23 publications

(9 citation statements)

References 44 publications

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“…DryadSynth [Huang et al 2020] and Duet [Lee 2021] have shown that integrating bottom-up and top-down approaches results in synthesizers greater than the sum of their parts, and we believe these findings would generalize to problems involving recursion. In particular, we think there is promising future work in integrating Burst with a top-down recursive synthesizer like SMyth.…”

Section: Future Workmentioning

confidence: 72%

Bottom-up synthesis of recursive functional programs using angelic execution

Miltner

Nunez

Brendel

et al. 2022

Proc. ACM Program. Lang.

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We present a novel bottom-up method for the synthesis of functional recursive programs. While bottom-up synthesis techniques can work better than top-down methods in certain settings, there is no prior technique for synthesizing recursive programs from logical specifications in a purely bottom-up fashion. The main challenge is that effective bottom-up methods need to execute sub-expressions of the code being synthesized, but it is impossible to execute a recursive subexpression of a program that has not been fully constructed yet. In this paper, we address this challenge using the concept of angelic semantics. Specifically, our method finds a program that satisfies the specification under angelic semantics (we refer to this as angelic synthesis), analyzes the assumptions made during its angelic execution, uses this analysis to strengthen the specification, and finally reattempts synthesis with the strengthened specification. Our proposed angelic synthesis algorithm is based on version space learning and therefore deals effectively with many incremental synthesis calls made during the overall algorithm. We have implemented this approach in a prototype called Burst and evaluate it on synthesis problems from prior work. Our experiments show that Burst is able to synthesize a solution to 94% of the benchmarks in our benchmark suite, outperforming prior work.

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Section: Future Workmentioning

confidence: 72%

Bottom-up synthesis of recursive functional programs using angelic execution

Miltner

Nunez

Brendel

et al. 2022

Proc. ACM Program. Lang.

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“…The solution quality can be roughly measured by the size of a solution program [50]. According to average and median sizes, solutions found by IGI-SBS and IGI-LGP have overall better quality than those found by GP and SA.…”

Section: B Results For List Manipulationmentioning

confidence: 99%

“…Program synthesis is an active research topic including a large and diverse body of work, such as enumerative program synthesis [5,40,50,51], constraint-based program synthesis [41,71,73] and neural program synthesis [17,22,38,61]. In what follows, we review some prior work on stochastic program synthesis that is most closely related to our proposed IGI.…”

Section: Related Workmentioning

confidence: 99%

Iterative Genetic Improvement: Scaling Stochastic Program Synthesis

Yuan¹,

Banzhaf²

2022

Preprint

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Program synthesis aims to automatically find programs from an underlying programming language that satisfy a given specification. While this has the potential to revolutionize computing, how to search over the vast space of programs efficiently is an unsolved challenge in program synthesis. In cases where large programs are required for a solution, it is generally believed that stochastic search has advantages over other classes of search techniques. Unfortunately, existing stochastic program synthesizers do not meet this expectation very well, suffering from the scalability issue. Here we propose a new framework for stochastic program synthesis, called iterative genetic improvement to overcome this problem, a technique inspired by the practice of the software development process.The key idea of iterative genetic improvement is to apply genetic improvement to improve a current reference program, and then iteratively replace the reference program by the best program found. Compared to traditional stochastic synthesis approaches, iterative genetic improvement can build up the complexity of programs incrementally in a more robust way. We evaluate the approach on two program synthesis domains: list manipulation and string transformation. Our empirical results indicate that this method has considerable advantages over several representative stochastic program synthesizer techniques, both in terms of scalability and of solution quality.

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“…The advantage of such a logic-based formalism is that it achieves a separation from solver and specification, which allows SyGuS to be solver-agnostic. Several different SyGuS solvers have been developed (e.g., [4,7,21,26]), many of which use drastically different internal algorithms that have different strengths for solving different kinds of problems. Moreover, a user of SyGuS need not consider the differing input languages or characteristics of these solvers, and instead can encode their problem just once in the SyGuS format to have access to all the different solvers.…”

Section: Why Isn't Existing Work In Synthesis Programmable?mentioning

confidence: 99%

“…For example, consider the problem of building an efficient enumeration algorithm for SemGuS, an algorithmic technique that is now successfully employed in most SyGuS solvers [2,4,21]. The success of enumeration has been driven by a number of clever ideas for efficiently pruning the search space of relevant programs.…”

Section: What Are We Working On Next?mentioning

confidence: 99%

Programmable Program Synthesis

D’Antoni

Kim

et al. 2021

Computer Aided Verification

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Program synthesis is now a reality, and we are approaching the point where domain-specific synthesizers can now handle problems of practical sizes. Moreover, some of these tools are finding adoption in industry. However, for synthesis to become a mainstream technique adopted at large by programmers as well as by end-users, we need to design programmable synthesis frameworks that (i) are not tailored to specific domains or languages, (ii) enable one to specify synthesis problems with a variety of qualitative and quantitative objectives in mind, and (iii) come equipped with theoretical as well as practical guarantees. We report on our work on designing such frameworks and on building synthesis engines that can handle program-synthesis problems describable in such frameworks, and describe open challenges and opportunities.

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

Cited by 23 publications

References 44 publications

Bottom-up synthesis of recursive functional programs using angelic execution

Bottom-up synthesis of recursive functional programs using angelic execution

Iterative Genetic Improvement: Scaling Stochastic Program Synthesis

Programmable Program Synthesis

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