The Automatic Inversion of Attribute Grammars

Over the last decade there has developed an acute awareness of the need to introduce abstraction and mathematical rigor into the programming process. This increased formality allows for the automatic manipulation of software, increasing productivity and, even more importantly, the manageability of complex systems. Along these lines, attribute grammars constitute a formal mechanism (or specifying translations between languages; from a Cormal description of the translation a translator can be automatically constructed. In this paper we consider taking this process one step further: given an attribute grammar specifying the translation from language L1 to the language L2, we address the question of whether the inverse attribute grammar specifying the inverse translation from L2 to L1 can be automatically generated. We show how to solve this problem for a restricted subset of attribute grammars. This inversion process allows for compatible two-way translators to be generated from a single description. To show the practical feasibility of attribute grammar inversion, we relate our experience in inverting an attribute grammar used as an interface for a formal database accessing language, SQL. The attribute grammar is used to paraphrase SQL database queries in English

Translating Between Programming Languages Using A Canonical Representation And Attribute Grammar Inversion

Automatic translation between programming languages is an important tool for increasing program reusability. Often the need arises to transport a large software system from one source language environment to another. Performing such a translation by hand is a large undertaking, costly in manpower and very error-prone. For this reason, several researchers have built automated tools to aid them in particular such projects [3, 1]. In this paper we present a new methodology for building source-to-source translators. This methodology involves designing a canonical form to represent programs of all source languages involved, and using attribute grammars (AGs) and automatic AG-inversion to build bidirectional translators between the various source languages and the canonical form. To test the feasibility of these ideas, we have created a system to translate between the C and Pascal programming languages

Simulating the Machine Translation of Low-Resource Languages by Designing a Translator Between English and an Artificially Constructed Language

Natural language processing (NLP), or the use of computers to analyze natural language, is a field that relies heavily on syntax. It would seem intuitive that computers would thrive in this area due to their strict syntax requirements, but the syntax of natural languages leaves them unable to properly parse and generate sentences that seem normal to the average speaker. A subfield of NLP, machine translation, works mainly to computerize translation between different languages. Unfortunately, such translation is not without its weaknesses; language documentation is not created equal, and many low-resource languages—languages with relatively few kinds of documentation, most often written—are left with no way to effectively benefit from machine translation. As a step toward better translation processors for low-resource languages, this thesis examined the possibility of machine translation between high resource languages and low resource languages through an analysis of different machine learning techniques, and ultimately constructing a simple translator between English and an artificially constructed language using a context-free grammar (CFG)

Web and Semantic Web Query Languages

A number of techniques have been developed to facilitate powerful data retrieval on the Web and Semantic Web. Three categories of Web query languages can be distinguished, according to the format of the data they can retrieve: XML, RDF and Topic Maps. This article introduces the spectrum of languages falling into these categories and summarises their salient aspects. The languages are introduced using common sample data and query types. Key aspects of the query languages considered are stressed in a conclusion

Discovering Models of Software Processes from Event-Based Data ; CU-CS-819-96

Many software process methods and tools presuppose the existence of a formal model of a process. Unfortunately, developing a formal model for an on-going, complex process can be dicult, costly, and error prone. This presents a practical barrier to the adoption of process technologies, which would be lowered by automated assistance in creating formal models. To this end, we have developed a data analysis technique that we term process discovery. Under this technique, data describing process events are rst captured from an on-going process and then used to generate a formal model of the behavior of that process. In this paper we describe a Markov method that we developed specically for process discovery, as well as describe two additional methods that we adopted from other domains and augmented for our purposes. The three methods range from the purely algorithmic to the purely statistical. We compare the methods and discuss their application in an industrial case study