SOFTWARE GENERATION BASED ON ATTRIBUTE GRAMMARS

In this paper a short overview is given of a software generator tool based on attribute grammars and the experiences are summarized with the use of this system for generating different types of software

Using decision trees to infer semantic functions of attribute grammars

In this paper we present a learning method called LAG (Learning of Attribute Grammar) which infers semantic functions for simple classes of attribute grammars by means of examples and background knowledge. This method is an improvement on the AGLEARN approach as it generates the training examples on its own via the effective use of background knowledge. The background knowledge is given in the form of attribute grammars. In addition, the LAG method employs the decision tree learner C4.5 during the learning process. Treating the specification of an attribute grammar as a learning task gives rise to the application of attribute grammars to new sorts of problems such as the Part-of-Speech (PoS) tagging of Hungarian sentences. Here we inferred context rules for selecting the correct annotations for ambiguous words with the help of a background attribute grammar. This attribute grammar detects structural correspondences of the sentences. The rules induced this way were found to be more precise than those rules learned without this information

Code Generation = A* + BURS

A system called BURS that is based on term rewrite systems and a search algorithm A* are combined to produce a code generator that generates optimal code. The theory underlying BURS is re-developed, formalised and explained in this work. The search algorithm uses a cost heuristic that is derived from the termrewrite system to direct the search. The advantage of using a search algorithm is that we need to compute only those costs that may be part of an optimal rewrite sequence

The debug slicing of logic programs

This paper extends the scope and optimality of previous algorithmic debugging techniques of Prolog programs using slicing techniques. We provide a dynamic slicing algorithm (called Debug slice) which augments the data flow analysis with control-flow dependences in order to identify the source of a bug included in a program. We developed a tool for debugging Prolog programs which also handles the specific programming techniques (cut, if-then, OR). This approach combines the Debug slice with Shapiro's algorithmic debugging technique