Quality of information and communication technology introduction

Software Quality Journal -     

Automatic Generation of Language-based Tools

Many tools can be automatically derived from formal language definitions, such as compilers/interpreters, editors, analyzers, visualizers/animators, etc. Some examples of language-based tools generated automatically by the LISA system are described in the paper. In addition the specification of an algorithm animator and program visualizer, Alma, generated from an extended LISA input-grammar is discussed; LISA principles and code are reused in Alma implementation.     

An educational tool for teaching compiler construction

Compiler construction is a well-developed discipline since there is a long tradition of producing compilers supported by practical underlying theory and a large selection of textbooks. In the compiler construction course, students learn how to write a compiler by hand and how to generate a compiler using tools like lex and yacc. However, these tools usually have little or no didactical value. In this paper, the software tool LISA is described. It facilitates learning and conceptual understanding of compiler construction in an efficient, direct, and long-lasting way. The authors' experience in using the tool shows the following didactical benefits: support for constructive learning, stimulation of exploratory and active learning, support for different learning styles and learning speed, increased motivation for learning, and better understanding of concepts.     

AN UNSUPERVISED INCREMENTAL LEARNING ALGORITHM FOR DOMAIN-SPECIFIC LANGUAGE DEVELOPMENT

While grammar inference (or grammar induction) has found extensive application in the areas of robotics, computational biology, and speech 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 domain-specific 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 extensions to the inference capabilities of GenInc, an incremental learning algorithm for inferring CFGs. We show that these extensions enable GenInc to infer more comprehensive grammars, discuss the results of applying GenInc to various domain-specific languages and evaluate the results using a comprehensive suite of grammar metrics.     

Grammar‐driven generation of domain‐specific language debuggers

Domain‐specific languages (DSLs) assist a software developer (or end‐user) in writing a program using idioms that are similar to the abstractions found in a specific problem domain. Tool support for DSLs is lacking when compared with the capabilities provided for standard general‐purpose languages (GPLs), such as Java and C++. For example, support for debugging a program written in a DSL is often non‐existent. The lack of a debugger at the proper abstraction level limits an end‐user's ability to discover and locate faults in a DSL program. This paper describes a grammar‐driven technique to build a debugging tool generation framework from existing DSL grammars. The DSL grammars are used to generate the hooks needed to interface with a supporting infrastructure constructed for an integrated development environment that assists in debugging a program written in a DSL. The contribution represents a coordinated approach to bring essential software tools (e.g. debuggers) to different types of DSLs (e.g. imperative, declarative, and hybrid). This approach hides from the end‐users the accidental complexities associated with expanding the focus of a language environment to include debuggers. The research described in this paper addresses a long‐term goal of empowering end‐users with development tools for particular DSL problem domains at the proper level of abstraction without depending on a specific GPL. Copyright © 2007 John Wiley & Sons, Ltd.     

A memetic grammar inference algorithm for language learning

An unsupervised incremental algorithm for grammar inference and its application to domain-specific language development are described. Grammatical inference is the process of learning a grammar from the set of positive and optionally negative sentences. Learning general context-free grammars is still considered a hard problem in machine learning and is not completely solved yet. The main contribution of the paper is a newly developed memetic algorithm, which is a population-based evolutionary algorithm enhanced with local search and a generalization process. The learning process is incremental since a new grammar is obtained from the current grammar and false negative samples, which are not parsed by the current grammar. Despite being incremental, the learning process is not sensitive to the order of samples. All important parts of this algorithm are explained and discussed. Finally, a case study of a domain specific language for rendering graphical objects is used to show the applicability of this approach.