Program sketching

Sketching is a synthesis methodology that aims to bridge the gap between a programmer’s high-level insights about a problem and the computer’s ability to manage low-level details. In sketching, the programmer uses a partial program, a sketch, to describe the desired implementation strategy, and leaves the low-level details of the implementation to an automated synthesis procedure. In order to generate an implementation from the programmer provided sketch, the synthesizer uses counterexample-guided inductive synthesis (CEGIS). Inductive synthesis refers to the process of generating candidate implementations from concrete examples of correct or incorrect behavior. CEGIS combines a SAT-based inductive synthesizer with an automated validation procedure, a bounded model-checker, that checks whether the candidate implementation produced by inductive synthesis is indeed correct and to produce new counterexamples. The result is a synthesis procedure that is able to handle complex problems from a variety of domains including ciphers, scientific programs, and even concurrent data-structures

Verifiable Reinforcement Learning via Policy Extraction

While deep reinforcement learning has successfully solved many challenging control tasks, its real-world applicability has been limited by the inability to ensure the safety of learned policies. We propose an approach to verifiable reinforcement learning by training decision tree policies, which can represent complex policies (since they are nonparametric), yet can be efficiently verified using existing techniques (since they are highly structured). The challenge is that decision tree policies are difficult to train. We propose VIPER, an algorithm that combines ideas from model compression and imitation learning to learn decision tree policies guided by a DNN policy (called the oracle) and its Q-function, and show that it substantially outperforms two baselines. We use VIPER to (i) learn a provably robust decision tree policy for a variant of Atari Pong with a symbolic state space, (ii) learn a decision tree policy for a toy game based on Pong that provably never loses, and (iii) learn a provably stable decision tree policy for cart-pole. In each case, the decision tree policy achieves performance equal to that of the original DNN policy

(Un)Decidability Results for Word Equations with Length and Regular Expression Constraints

We prove several decidability and undecidability results for the satisfiability and validity problems for languages that can express solutions to word equations with length constraints. The atomic formulas over this language are equality over string terms (word equations), linear inequality over the length function (length constraints), and membership in regular sets. These questions are important in logic, program analysis, and formal verification. Variants of these questions have been studied for many decades by mathematicians. More recently, practical satisfiability procedures (aka SMT solvers) for these formulas have become increasingly important in the context of security analysis for string-manipulating programs such as web applications. We prove three main theorems. First, we give a new proof of undecidability for the validity problem for the set of sentences written as a forall-exists quantifier alternation applied to positive word equations. A corollary of this undecidability result is that this set is undecidable even with sentences with at most two occurrences of a string variable. Second, we consider Boolean combinations of quantifier-free formulas constructed out of word equations and length constraints. We show that if word equations can be converted to a solved form, a form relevant in practice, then the satisfiability problem for Boolean combinations of word equations and length constraints is decidable. Third, we show that the satisfiability problem for quantifier-free formulas over word equations in regular solved form, length constraints, and the membership predicate over regular expressions is also decidable.Comment: Invited Paper at ADDCT Workshop 2013 (co-located with CADE 2013

SyGuS-Comp 2016: Results and Analysis

Syntax-Guided Synthesis (SyGuS) is the computational problem of finding an implementation f that meets both a semantic constraint given by a logical formula $\varphi$ in a background theory T, and a syntactic constraint given by a grammar G, which specifies the allowed set of candidate implementations. Such a synthesis problem can be formally defined in SyGuS-IF, a language that is built on top of SMT-LIB. The Syntax-Guided Synthesis Competition (SyGuS-Comp) is an effort to facilitate, bring together and accelerate research and development of efficient solvers for SyGuS by providing a platform for evaluating different synthesis techniques on a comprehensive set of benchmarks. In this year's competition we added a new track devoted to programming by examples. This track consisted of two categories, one using the theory of bit-vectors and one using the theory of strings. This paper presents and analyses the results of SyGuS-Comp'16.Comment: In Proceedings SYNT 2016, arXiv:1611.07178. arXiv admin note: text overlap with arXiv:1602.0117

SPT: Storyboard Programming Tool

We present Spt, a tool that helps programmers write low-level data-structure manipulations by combining various forms of insights such as abstract and concrete input-output examples as well as implementation skeletons. When programmers write such manipulations, they typically have a clear high-level intuition about how the manipulation should work, but implementing efficient low-level pointer manipulating code is error-prone. Our tool aims to bridge the gap between the intuition and the corresponding implementation by automatically synthesizing the implementation. The tool frames the synthesis problem as a generalization of an abstract-interpretation based shape analysis, and represents the problem as a set of constraints which are solved efficiently by the Sketch solver. We report the successful evaluation of our tool on synthesizing several linked list and binary search tree manipulations.National Science Foundation (U.S.) (Grant CCF-1116362