SAGA: A project to automate the management of software production systems

The project to automate the management of software production systems is described. The SAGA system is a software environment that is designed to support most of the software development activities that occur in a software lifecycle. The system can be configured to support specific software development applications using given programming languages, tools, and methodologies. Meta-tools are provided to ease configuration. Several major components of the SAGA system are completed to prototype form. The construction methods are described

A Monitoring Language for Run Time and Post-Mortem Behavior Analysis and Visualization

UFO is a new implementation of FORMAN, a declarative monitoring language, in which rules are compiled into execution monitors that run on a virtual machine supported by the Alamo monitor architecture.Comment: In M. Ronsse, K. De Bosschere (eds), proceedings of the Fifth International Workshop on Automated Debugging (AADEBUG 2003), September 2003, Ghent. cs.SE/030902

SAGA: A project to automate the management of software production systems

The Software Automation, Generation and Administration (SAGA) project is investigating the design and construction of practical software engineering environments for developing and maintaining aerospace systems and applications software. The research includes the practical organization of the software lifecycle, configuration management, software requirements specifications, executable specifications, design methodologies, programming, verification, validation and testing, version control, maintenance, the reuse of software, software libraries, documentation, and automated management

Scaffolding in Narrative Learning: Appraisal Analysis in Teachers' Talk

Engagement is one of appraisal dimensions introduced by Martin and White (2005) that is used to analyze the stances which a teacher takes, both in relation to the students and the visual-verbal components in texts and the way of the teacher align or dis-align the students. This paper explores the interaction between a teacher and thirty-two junior high school students in learning narrative texts. The focus of this study is on the stages of scaffolding to help the students to cope with narrative texts. This study employs classroom discourse analysis particularly appraisal analysis on engagement elements in teacher's talk. The findings of this study depict that in teaching the students, the teacher uses different kinds of engagement systems of heterogloss (contract and expand) to take particular stance to mediate the students with teaching materials. The heterogloss is also used to guide the students in exploring the learning materials. Practically, the result of the study is beneficial for the EFL teachers as a reference in teaching narrative texts

Statistical Function Tagging and Grammatical Relations of Myanmar Sentences

This paper describes a context free grammar (CFG) based grammatical relations for Myanmar sentences which combine corpus-based function tagging system. Part of the challenge of statistical function tagging for Myanmar sentences comes from the fact that Myanmar has free-phrase-order and a complex morphological system. Function tagging is a pre-processing step to show grammatical relations of Myanmar sentences. In the task of function tagging, which tags the function of Myanmar sentences with correct segmentation, POS (part-of-speech) tagging and chunking information, we use Naive Bayesian theory to disambiguate the possible function tags of a word. We apply context free grammar (CFG) to find out the grammatical relations of the function tags. We also create a functional annotated tagged corpus for Myanmar and propose the grammar rules for Myanmar sentences. Experiments show that our analysis achieves a good result with simple sentences and complex sentences.Comment: 16 pages, 7 figures, 8 tables, AIAA-2011 (India). arXiv admin note: text overlap with arXiv:0912.1820 by other author

Exploiting multi-word units in history-based probabilistic generation

We present a simple history-based model for sentence generation from LFG f-structures, which improves on the accuracy of previous models by breaking down PCFG independence assumptions so that more f-structure conditioning context is used in the prediction of grammar rule expansions. In addition, we present work on experiments with named entities and other multi-word units, showing a statistically significant improvement of generation accuracy. Tested on section 23 of the PennWall Street Journal Treebank, the techniques described in this paper improve BLEU scores from 66.52 to 68.82, and coverage from 98.18% to 99.96%