Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
Relational Synthesis for Pattern Matching. / Kosarev, Dmitry; Lozov, Petr; Boulytchev, Dmitry.
Programming Languages and Systems - 18th Asian Symposium, APLAS 2020, Proceedings. ред. / Bruno C. Oliveira. Springer Nature, 2020. стр. 293-310 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Том 12470 LNCS).Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
}
TY - GEN
T1 - Relational Synthesis for Pattern Matching
AU - Kosarev, Dmitry
AU - Lozov, Petr
AU - Boulytchev, Dmitry
N1 - Funding Information: The reported study was funded by RFBR, projects number 18-01-00380 and 19-31-90053. 1 We have to note that this term is overloaded and can be used to refer to completely different approaches than we utilize. 2 http://minikanren.org. Publisher Copyright: © 2020, Springer Nature Switzerland AG. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020
Y1 - 2020
N2 - We present a completely declarative approach to synthesizing pattern matching construct implementations based on application of relational programming, a specific form of constraint logic programming. Our approach is based on relational representations of both the high-level semantics of pattern matching and the semantics of an intermediate-level implementation language. This choice makes our approach, in principle, very scalable as we only need to modify the high-level semantics in order to synthesize the implementation of a pattern matching new feature. Our evaluation on a set of small samples, partially taken from existing literature shows, that our framework is capable of synthesizing optimal implementations quickly. Our in-depth stress evaluation on a number of artificial benchmarks, however, has shown the need for future improvements.
AB - We present a completely declarative approach to synthesizing pattern matching construct implementations based on application of relational programming, a specific form of constraint logic programming. Our approach is based on relational representations of both the high-level semantics of pattern matching and the semantics of an intermediate-level implementation language. This choice makes our approach, in principle, very scalable as we only need to modify the high-level semantics in order to synthesize the implementation of a pattern matching new feature. Our evaluation on a set of small samples, partially taken from existing literature shows, that our framework is capable of synthesizing optimal implementations quickly. Our in-depth stress evaluation on a number of artificial benchmarks, however, has shown the need for future improvements.
KW - Pattern matching
KW - Relational interpreters
KW - Relational programming
UR - http://www.scopus.com/inward/record.url?scp=85097650562&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-64437-6_15
DO - 10.1007/978-3-030-64437-6_15
M3 - Conference contribution
AN - SCOPUS:85097650562
SN - 9783030644369
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 293
EP - 310
BT - Programming Languages and Systems - 18th Asian Symposium, APLAS 2020, Proceedings
A2 - Oliveira, Bruno C.
PB - Springer Nature
T2 - 18th Asian Symposium on Programming Languages and Systems, APLAS 2020
Y2 - 30 November 2020 through 2 December 2020
ER -
ID: 76606597