Dark knowledge and graph grammars in automated software design
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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