Software system documentation is almost always expressed informally, in natural language and free text. Examples include requirement specifications, design documents, manual pages, system development journals, error logs and related maintenance reports. We propose an approach to establish and maintain traceability links between the source code and free text documents. A premise of our work is that programmers use meaningful names for program's items, such as functions, variables, types, classes, and methods. We believe that the application-domain knowledge that programmers process when writing the code is often captured by the mnemonics for identifiers; therefore, the analysis of these mnemonics can help to associate high level concepts with program concepts, and vice-versa. In this paper, the approach is applied to software written in an object-oriented language, namely C++, to trace classes to manual sections. Keywords: traceability, versions compliance check, object orientation 1.

This paper refers to a machine-mediated person-to-person multilingual communication system. Stress is put on robustness, that is the ability of the system to preserve communication even in presence of the variability and errors typical of spoken language systems. The statistical approach is adopted not only at the acoustic level, but also for the linguistic processing. Therefore, while an overview of the global architecture will be briefly introduced, the focus will be put on the acoustic recognizer and the understanding module. Experimental evaluations complete the presentation. 1. INTRODUCTION This paper refers to a machine-mediated person-to-person multilingual communication system. The scenario involves appointment negotiation between two persons speaking different languages. The focus will be put on two of the system modules, while an overview of the global architecture will be briefly introduced. The expression multilingual communication is preferred to speech-to-speech translat...

ESMERALDA is an integrated environment for the development of speech recognition systems. It provides a powerful selection of methods for building statistical models together with an efficient incremental recognizer. In this paper the approaches adopted for estimating mixture densities, Hidden Markov Models, and n-gram language models are described as well as the algorithms applied during recognition. Evaluation results on a speaker independent spontaneous speech recognition task demonstrate the capabilities of ESMERALDA.