Model Transformation By Example (MTBE) is a new branch of model driven software development. Transducers (automata with output) can be used to abstract model transformation. This form of abstraction makes possible to consider applications of grammatical inference algorithms. In this paper we investigate whether an effective inference procedure can be developed to derive a modified transducer from examples of desired input/output pairs instead of infering such a transducer from scratch. The paper starts with the description of our motivational example. Then we propose an algorithm to infer modifications of the transducers. Finally, we discuss metrics to evaluate the quality

Abstract- While grammar inference (or grammar induction) has found extensive application in the areas of robotics, computational biology, speech and pattern recognition, its application to problems in programming language and software engineering domains has been limited. We have found a new application area for grammar inference which intends to make domainspecific language development easier for domain experts not well versed in programming language design, and finds a second application in construction of renovation tools for legacy software systems. As a continuation of our previous efforts to infer context-free grammars (CFGs) for domain-specific languages which previously involved a genetic-programming based CFG inference system, we discuss improvements made to an incremental learning algorithm, called GenInc, for inferring context-free grammars with a core focus on facilitating domain-specific language development. We elaborate on the enhancements made to GenInc in the form of new operators, and conclude by discussing the results of applying GenInc to domain-specific languages.

The UML has grown in popularity as the standard modeling language for describing software applications. However, UML lacks the formalism of a rigid semantics, which can lead to ambiguities in understanding the specifications. We propose a grammar-based approach to validating class diagrams and illustrate this technique using a simple case-study. Our technique involves converting UML representations into an equivalent grammar form, and then using existing language transformation and development tools to assist in the validation process. A string comparison metric is also used which provides feedback, allowing the user to modify the original class diagram according to the functionality desired.

Grammar Inference is the process of learning a grammar from examples, either positive (i.e., the grammar generates the string) and/or negative (i.e., the grammar does not generate the string). Although grammar inference has been successfully applied to many diverse domains such as speech recognition and robotics, its application to software engineering has been limited. This research investigates the applicability of grammar inference to software engineering and programming language development challenge problems, where grammar inference offers an innovative solution to the problem, while remaining tractable and within the scope of that problem. Specifically, the following challenges are addressed in this research: 1. Recovery of a metamodel from instance models: Within the area of domain-specific modeling (DSM), instance models may evolve independently of the original metamodel resulting in metamodel drift, an inconsistency between the instance model and the associated metamodel such that the instance model may no longer be loaded