Predicting a Correct Program in Programming by Example

2015 ◽  
pp. 398-414 ◽  
Author(s):  
Rishabh Singh ◽  
Sumit Gulwani
Keyword(s):  
Programming By Example ◽  
Correct Program

scholarly journals Correct program parallelisations

2021 ◽  
Author(s):  
S. Blom ◽  
S. Darabi ◽  
M. Huisman ◽  
M. Safari
Keyword(s):  
Composition Operators ◽  
Parallel Programs ◽  
Parallel Program ◽  
Tool Support ◽  
Intermediate Representation ◽  
Data Race ◽  
Sequential Program ◽  
Functional Correctness ◽  
Correct Program ◽  
Basic Blocks

AbstractA commonly used approach to develop deterministic parallel programs is to augment a sequential program with compiler directives that indicate which program blocks may potentially be executed in parallel. This paper develops a verification technique to reason about such compiler directives, in particular to show that they do not change the behaviour of the program. Moreover, the verification technique is tool-supported and can be combined with proving functional correctness of the program. To develop our verification technique, we propose a simple intermediate representation (syntax and semantics) that captures the main forms of deterministic parallel programs. This language distinguishes three kinds of basic blocks: parallel, vectorised and sequential blocks, which can be composed using three different composition operators: sequential, parallel and fusion composition. We show how a widely used subset of OpenMP can be encoded into this intermediate representation. Our verification technique builds on the notion of iteration contract to specify the behaviour of basic blocks; we show that if iteration contracts are manually specified for single blocks, then that is sufficient to automatically reason about data race freedom of the composed program. Moreover, we also show that it is sufficient to establish functional correctness on a linearised version of the original program to conclude functional correctness of the parallel program. Finally, we exemplify our approach on an example OpenMP program, and we discuss how tool support is provided.

scholarly journals Guiding dynamic programing via structural probability for accelerating programming by example

2020 ◽  
Vol 4 (OOPSLA) ◽  
pp. 1-29
Author(s):  
Ruyi Ji ◽  
Yican Sun ◽  
Yingfei Xiong ◽  
Zhenjiang Hu
Keyword(s):  
Dynamic Programing ◽  
Programming By Example

AESOP: Adaptive Event detection SOftware using Programming by example

2015 ◽  
Author(s):  
Ashwin Thangali ◽  
Harsha Prasad ◽  
Sai Kethamakka ◽  
David Demirdjian ◽  
Neal Checka
Keyword(s):  
Event Detection ◽  
Programming By Example ◽  
Detection Software

Programming by Example

2011 ◽  
pp. 805-805
Author(s):  
Thomas Zeugmann ◽  
Pascal Poupart ◽  
James Kennedy ◽  
Xin Jin ◽  
Jiawei Han ◽  
...  
Keyword(s):  
Programming By Example

scholarly journals Adaptive Test Selection for Factorization-based Surrogate Fitness in Genetic Programming

2017 ◽  
Vol 42 (4) ◽  
pp. 339-358 ◽  
Author(s):  
Krzysztof Krawiec ◽  
Paweł Liskowski
Keyword(s):  
Genetic Programming ◽  
Evolutionary Algorithm ◽  
Matrix Factorization ◽  
Fitness Function ◽  
Interaction Matrix ◽  
Simulated Evolution ◽  
Adaptive Test ◽  
Selection For ◽  
Correct Program ◽  
Non Negative Matrix Factorization

Abstract Genetic programming (GP) is a variant of evolutionary algorithm where the entities undergoing simulated evolution are computer programs. A fitness function in GP is usually based on a set of tests, each of which defines the desired output a correct program should return for an exemplary input. The outcomes of interactions between programs and tests in GP can be represented as an interaction matrix, with rows corresponding to programs in the current population and columns corresponding to tests. In previous work, we proposed SFIMX, a method that performs only a fraction of interactions and employs non-negative matrix factorization to estimate the outcomes of remaining ones, shortening GP’s runtime. In this paper, we build upon that work and propose three extensions of SFIMX, in which the subset of tests drawn to perform interactions is selected with respect to test difficulty. The conducted experiment indicates that the proposed extensions surpass the original SFIMX on a suite of discrete GP benchmarks.

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