Guest editors’ introduction to the special section from the international conference on software maintenance and evolution

IEEE Transactions on Software Engineering, 33(12): pp. 797-798

PICES Press, Vol. 18, No. 1, Winter 2010

•Major Outcomes from the 2009 PICES Annual Meeting: A Note from the Chairman (pp. 1-3, 8) •PICES Science – 2009 (pp. 4-8) •2009 PICES Awards (pp. 9-10) •New Chairmen in PICES (pp. 11-15) •PICES Interns (p. 15) •The State of the Western North Pacific in the First Half of 2009 (pp. 16-17, 27) •The State of the Northeast Pacific in 2009 (pp. 18-19) •The Bering Sea: Current Status and Recent Events (pp. 20-21) •2009 PICES Summer School on “Satellite Oceanography for the Earth Environment” (pp. 22-25) •2009 International Conference on “Marine Bioinvasions” (pp. 26-27) •A New PICES Working Group Holds Workshop and Meeting in Jeju Island (pp. 28-29) •The Second Marine Ecosystem Model Inter-comparison Workshop (pp. 30-32) •ICES/PICES/UNCOVER Symposium on “Rebuilding Depleted Fish Stocks – Biology, Ecology, Social Science and Management Strategies” (pp. 33-35) •2009 North Pacific Synthesis Workshop (pp. 36-37) •2009 PICES Rapid Assessment Survey (pp. 38-40

A Model-Based Approach to Impact Analysis Using Model Differencing

Impact analysis is concerned with the identification of consequences of changes and is therefore an important activity for software evolution. In modelbased software development, models are core artifacts, which are often used to generate essential parts of a software system. Changes to a model can thus substantially affect different artifacts of a software system. In this paper, we propose a modelbased approach to impact analysis, in which explicit impact rules can be specified in a domain specific language (DSL). These impact rules define consequences of designated UML class diagram changes on software artifacts and the need of dependent activities such as data evolution. The UML class diagram changes are identified automatically using model differencing. The advantage of using explicit impact rules is that they enable the formalization of knowledge about a product. By explicitly defining this knowledge, it is possible to create a checklist with hints about development steps that are (potentially) necessary to manage the evolution. To validate the feasibility of our approach, we provide results of a case study.Comment: 16 pages, 5 figures, In: Proceedings of the 8th International Workshop on Software Quality and Maintainability (SQM), ECEASST Journal, vol. 65 201

Focal Spot, Summer 1986

https://digitalcommons.wustl.edu/focal_spot_archives/1043/thumbnail.jp

SOUND SOFTWARE: TOWARDS SOFTWARE REUSE IN AUDIO AND MUSIC RESEARCH

© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Proceedings of the National Conference on Managing Irrigation for Environmentally Sustainable Agriculture in Pakistan, Islamabad, November 5-7, 1996. Volume I - Inauguration and deliberations

Irrigation managementSustainable agricultureEnvironmental effectsIrrigation canalsFarmer managed irrigation systemsWater distributionIrrigation efficiencyDrainageHydrologyWater reuseTube wellsSoil salinityWater tableWaterloggingGroundwater managementIrrigated farmingInstitution building

Information systems and healthcare XXIV: Factors affecting the EAI adoption in the healthcare sector

Recent developments in the field of integration technologies like Enterprise Application Integration (EAI) have emerged to support organizations towards improving the quality of services and reducing integration costs. Despite the importance of EAI, there is limited empirical research reported on its adoption in the healthcare sector. Khoumbati et al. [2006] developed a model for the evaluation of EAI in healthcare organizations. In doing so, the causal interrelationship of EAI adoption factors was identified by using fuzzy cognitive mapping. This paper is a progression of previous work in the area and seeks to contribute by validating the model through a different case environment. Thus, this paper contributes by deriving and proposing the MAESTRO model for EAI adoption. MAESTRO identifies a set of factors that influence EAI adoption and it is evaluated through a real-life case study. It provides an understanding of the EAI adoption process through its grounding on empirical data. In doing so, the MAESTRO model supports the management of healthcare organizations during the decision-making process for EAI adoption

UNM School of Law Library Annual Report 2015-2016

The annual report for the University of New Mexico School of Law & Law Journals for the period July 1, 2015 through June 30, 2016