Evaluating knowledge representations for program characterization

Abstract

Knowledge representation attempts to organize the knowledge of a context in order for automated systems to utilize it to solve complex problems. Among several difficult problems, one worth mentioning is called code-generation, which is undecidable due to its complexity. A technique to mitigate this problem is to represent the knowledge and use an automatic reasoning system to infer an acceptable solution. This article evaluates knowledge representations for program characterization for the context of code-generation systems. The experimental results prove that program Numerical Features as knowledge representation can achieve 85% near to the best possible results. Furthermore, such results demonstrate that an automatic code-generating system, which uses this knowledge representation is capable to obtain performance better than others codegenerating systems.