<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Batory, Don</style></author><author><style face="normal" font="default" size="100%">Rui C. Gonçalves</style></author><author><style face="normal" font="default" size="100%">Marker, Bryan</style></author><author><style face="normal" font="default" size="100%">Siegmund, Janet</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Dark Knowledge and Graph Grammars in Automated Software Design</style></title><secondary-title><style face="normal" font="default" size="100%">SLE '13: Proceeding of the 6th International Conference on Software Language Engineering</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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.&lt;/p&gt;
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