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Groote, Jan Friso; Warners, Joost P.

doi:10.1023/a:1006366304347

Cited by 31 publications

(2 citation statements)

References 27 publications

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“…1 indicates that "sum" or "any" needs to be selected, and tokens "for i in input if" are used together in either case. This representation resembles the propositional formulas composed of "AND" and "OR" [16].…”

Section: Input #2: Commands To Use In Synthesized Programsmentioning

confidence: 99%

“…The search begins by calling the function find_program(). It uses a while loop to examine one program in each iteration (lines [14][15][16][17] and terminates either when it discovers a target program (lines 15-16) 3 or when it has no more program to explore (line 18). To confirm that a program complies with Input #1, we run the program with input data as described in Input #1, and observe whether it produces the same output as Input #1.…”

Section: ) Navigation Through Possible Programsmentioning

confidence: 99%

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Program Synthesis Through Learning the Input-Output Behavior of Commands

2022

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Program synthesis writes programs on behalf of humans, increasing software development productivity. Existing systems select a proper sequence of commands by assembling them in various ways and analyzing their meanings, which is accurate and fast when a detailed specification is given for each available command, including the syntax and semantics. However, preparing this specification is burdensome and must be repeated to add or modify available commands. We propose a synthesis system that requires moderate groundwork to achieve sufficient accuracy and speed. The proposed system receives the syntax of the available commands and learns their meanings independently by writing programs and observing their input-output behavior. Using the learned knowledge, the system selects a likely sequence of commands and gradually revises them to converge to a target program. We validated the system by synthesizing 1,000 integer-manipulation programs out of 245,410 possible programs. The system synthesized most of the programs within 1,000 revisions, which is 80% faster than the state-of-the-art system based on machine-learned input-output patterns. In addition, greater than 10% of the programs were synthesized within several revisions. We believe that the proposed system provides a basis for synthesis systems based on learning input-output behavior.

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Section: Input #2: Commands To Use In Synthesized Programsmentioning

confidence: 99%

Section: ) Navigation Through Possible Programsmentioning

confidence: 99%

Program Synthesis Through Learning the Input-Output Behavior of Commands

2022

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Theorem Proving for Verification

Rushby

2001

Lecture Notes in Computer Science

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The Quest for Efficient Boolean Satisfiability Solvers

Zhang

Malik

2002

Automated Deduction—CADE-18

128

160

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Abstract. The classical NP-complete problem of Boolean Satisfiability (SAT) has seen much interest in not just the theoretical computer science community, but also in areas where practical solutions to this problem enable significant practical applications. Since the first development of the basic search based algorithm proposed by Davis, Putnam, Logemann and Loveland (DPLL) about forty years ago, this area has seen active research effort with many interesting contributions that have culminated in state-of-the-art SAT solvers today being able to handle problem instances with thousands, and in same cases even millions, of variables. In this paper we examine some of the main ideas along this passage that have led to our current capabilities. Given the depth of the literature in this field, it is impossible to do this in any comprehensive way; rather we focus on techniques with consistent demonstrated efficiency in available solvers. For the most part, we focus on techniques within the basic DPLL search framework, but also briefly describe other approaches and look at some possible future research directions.

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Untitled

Cited by 31 publications

References 27 publications

Program Synthesis Through Learning the Input-Output Behavior of Commands

Program Synthesis Through Learning the Input-Output Behavior of Commands

Theorem Proving for Verification

The Quest for Efficient Boolean Satisfiability Solvers

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