Dark knowledge and graph grammars in automated software design

dc.contributor.author Batory,D en
dc.contributor.author Rui Carlos Gonçalves en
dc.contributor.author Marker,B en
dc.contributor.author Siegmund,J en
dc.date.accessioned 2018-02-24T20:41:55Z
dc.date.available 2018-02-24T20:41:55Z
dc.date.issued 2013 en
dc.description.abstract Mechanizing the development of hard-to-write and costly-to-maintain software is the core problem of automated software design. Encoding expert knowledge (a.k.a. dark knowledge) about a software domain is central to its solution. We assert that a solution can be cast in terms of the ideas of language design and engineering. Graph grammars can be a foundation for modern automated software development. The sentences of a grammar are designs of complex dataflow systems. We explain how graph grammars provide a framework to encode expert knowledge, produce correct-by-construction derivations of dataflow applications, enable the generation of high-performance code, and improve how software design of dataflow applications can be taught to undergraduates. © 2013 Springer International Publishing. en
dc.identifier.uri http://repositorio.inesctec.pt/handle/123456789/7515
dc.identifier.uri http://dx.doi.org/10.1007/978-3-319-02654-1_1 en
dc.language eng en
dc.relation 6487 en
dc.rights info:eu-repo/semantics/openAccess en
dc.title Dark knowledge and graph grammars in automated software design en
dc.type conferenceObject en
dc.type Publication en
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