Program synthesis: challenges and opportunities

David, Cristina; Kroening, Daniel

doi:10.1098/rsta.2015.0403

Cited by 23 publications

(15 citation statements)

References 48 publications

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“…In contrast to SyGuS, our program synthesiser is general-purpose as it has an universal target language such that no syntactic restriction of the output needs to be provided. A more detailed comparison of these different directions in program synthesis as well as an investigation of current challenges in the field can be found in [34].…”

Section: Comparison To Sygusmentioning

confidence: 99%

Program Synthesis for Program Analysis

David

Kesseli

Kroening

et al. 2018

ACM Trans. Program. Lang. Syst.

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In this paper, we propose a unified framework for designing static analysers based on program synthesis. For this purpose, we identify a fragment of second-order logic with restricted quantification that is expressive enough to model numerous static analysis problems (e.g., safety proving, bug finding, termination and non-termination proving, refactoring). As our focus is on programs that use bit-vectors, we build a decision procedure for this fragment over finite domains in the form of a program synthesiser. We provide instantiations of our framework for solving a diverse range of program verification tasks such as termination, non-termination, safety and bug finding, superoptimisation and refactoring. Our experimental results show that our program synthesiser compares positively with specialised tools in each area as well as with general-purpose synthesisers.

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Section: Comparison To Sygusmentioning

confidence: 99%

Program Synthesis for Program Analysis

David

Kesseli

Kroening

et al. 2018

ACM Trans. Program. Lang. Syst.

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“…Contributions We apply an inductive synthesis framework, known as Counter-Example Guided Inductive Synthesis (CEGIS) [3,4] and recently employed in a number of control applications [5,6,7,8], to construct Lyapunov functions for linear, polynomial and parametric ODEs, and (for non-linear ODEs) to constructively characterise their domain of validity. CEGIS, originally developed for program synthesis based on the satisfiability of second-order logical formulae, is employed in this work with template Lyapunov functions and in conjunction with a Satisfiability Modulo Theory (SMT) solver [9].…”

Section: Introductionmentioning

confidence: 99%

Tools and Algorithms for the Construction and Analysis of Systems

Vojnar¹,

Zhang²

2020

Lecture Notes in Computer Science

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In this paper we employ SMT solvers to soundly synthesise Lyapunov functions that assert the stability of a given dynamical model. The search for a Lyapunov function is framed as the satisfiability of a second-order logical formula, asking whether there exists a function satisfying a desired specification (stability) for all possible initial conditions of the model. We synthesise Lyapunov functions for linear, non-linear (polynomial), and for parametric models. For non-linear models, the algorithm also determines a region of validity for the Lyapunov function. We exploit an inductive framework to synthesise Lyapunov functions, starting from parametric templates. The inductive framework comprises two elements: a learner proposes a Lyapunov function, and a verifier checks its validity-its lack is expressed via a counterexample (a point over the state space), for further use by the learner. Whilst the verifier uses the SMT solver Z3, thus ensuring the overall soundness of the procedure, we examine two alternatives for the learner: a numerical approach based on the optimisation tool Gurobi, and a sound approach based again on Z3. The overall technique is evaluated over a broad set of benchmarks, which shows that this methodology not only scales to 10-dimensional models within reasonable computational time, but also offers a novel soundness proof for the generated Lyapunov functions and their domains of validity.

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“…A technique of genetic programming (GP) [17,18] is an algorithm to optimize structured data based on a evolutionary algorithm (EA) [11,25]. GP is applied to various fields such as program synthesis [5], function generations [14] and rule set discoveries [30]. Although GP is very effective for optimizing structured data, it has several problems such as getting into a bloat, inadequate optimization of constant nodes, being easily captured in local optimal solution area when applied to complicated problems.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective genetic programming with partial sampling and its extension to many-objective

Ohki

2019

SN Appl. Sci.

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This paper describes a technique on an optimization of tree-structure data by of multi-objective evolutionary algorithm, or multi-objective genetic programming. GP induces bloat of the tree structure as one of the major problem. The cause of bloat is that the tree structure obtained by the crossover operator grows bigger and bigger but its evaluation does not improve. To avoid the risk of bloat, a partial sampling operator is proposed as a mating operator. The size of the tree and a structural distance are introduced into the measure of the tree-structure data as the objective functions in addition to the index of the goodness of tree structure. GP is defined as a three-objective optimization problem. SD is also applied for the ranking of parent individuals instead to the crowding distance of the conventional NSGA-II. When the index of the goodness of tree-structure data is two or more, the number of objective functions in the above problem becomes four or more. We also propose an effective many-objective EA applicable to such the many-objective GP. We focus on NSGA-II based on Pareto partial dominance (NSGA-II-PPD). NSGA-II-PPD requires beforehand a combination list of the number of objective functions to be used for Pareto partial dominance (PPD). The contents of the combination list greatly influence the optimization result. We propose to schedule a parameter r meaning the subset size of objective functions for PPD and to eliminate individuals created by the mating having the same contents as the individual of the archive set.

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Program synthesis: challenges and opportunities

Cited by 23 publications

References 48 publications

Program Synthesis for Program Analysis

Program Synthesis for Program Analysis

Tools and Algorithms for the Construction and Analysis of Systems

Multi-objective genetic programming with partial sampling and its extension to many-objective

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