Data-Flow Modeling: A Survey of Issues and Approaches

This paper presents a survey of previous research on modeling the data flow perspective of business processes. When it comes to modeling and analyzing business process models the current research focuses on control flow modeling (i.e. the activities of the process) and very little attention is paid to the data-flow perspective. But data is essential in a process. In order to execute a workflow, the tasks need data. Without data or without data available on time, the control flow cannot be executed. For some time, various researchers tried to investigate the data flow perspective of process models or to combine the control and data flow in one model. This paper surveys those approaches. We conclude that there is no model showing a clear data flow perspective focusing on how data changes during a process execution. The literature offers some similar approaches ranging from data modeling using elements from relational database domain, going through process model verification and ending with elements related to Web Services

OpenUP/MDRE: A Model-Driven Requirements Engineering Approach for Health-Care Systems

The domains and problems for which it would be desirable to introduce information systems are currently very complex and the software development process is thus of the same complexity. One of these domains is health-care. Model-Driven Development (MDD) and Service-Oriented Architecture (SOA) are software development approaches that raise to deal with complexity, to reduce time and cost of development, augmenting flexibility and interoperability. However, many techniques and approaches that have been introduced are of little use when not provided under a formalized and well-documented methodological umbrella. A methodology gives the process a well-defined structure that helps in fast and efficient analysis and design, trouble-free implementation, and finally results in the software product improved quality. While MDD and SOA are gaining their momentum toward the adoption in the software industry, there is one critical issue yet to be addressed before its power is fully realized. It is beyond dispute that requirements engineering (RE) has become a critical task within the software development process. Errors made during this process may have negative effects on subsequent development steps, and on the quality of the resulting software. For this reason, the MDD and SOA development approaches should not only be taken into consideration during design and implementation as usually occurs, but also during the RE process. The contribution of this dissertation aims at improving the development process of health-care applications by proposing OpenUP/MDRE methodology. The main goal of this methodology is to enrich the development process of SOA-based health-care systems by focusing on the requirements engineering processes in the model-driven context. I believe that the integration of those two highly important areas of software engineering, gathered in one consistent process, will provide practitioners with many benets. It is noteworthy that the approach presented here was designed for SOA-based health-care applications, however, it also provides means to adapt it to other architectural paradigms or domains. The OpenUP/MDRE approach is an extension of the lightweight OpenUP methodology for iterative, architecture-oriented and model-driven software development. The motivation for this research comes from the experience I gained as a computer science professional working on the health-care systems. This thesis also presents a comprehensive study about: i) the requirements engineering methods and techniques that are being used in the context of the model-driven development, ii) known generic but flexible and extensible methodologies, as well as approaches for service-oriented systems development, iii) requirements engineering techniques used in the health-care industry. Finally, OpenUP/MDRE was applied to a concrete industrial health-care project in order to show the feasibility and accuracy of this methodological approach.Loniewski, G. (2010). OpenUP/MDRE: A Model-Driven Requirements Engineering Approach for Health-Care Systems. http://hdl.handle.net/10251/11652Archivo delegad

A Reference Architecture for Provisioning of Tools as a Service: Meta-Model, Ontologies and Design Elements

Abstract not availableMuhammad Aufeef Chauhan, Muhammad Ali Babar, Quan Z. Shen

Preserving the Quality of Architectural Tactics in Source Code

In any complex software system, strong interdependencies exist between requirements and software architecture. Requirements drive architectural choices while also being constrained by the existing architecture and by what is economically feasible. This makes it advisable to concurrently specify the requirements, to devise and compare alternative architectural design solutions, and ultimately to make a series of design decisions in order to satisfy each of the quality concerns. Unfortunately, anecdotal evidence has shown that architectural knowledge tends to be tacit in nature, stored in the heads of people, and lost over time. Therefore, developers often lack comprehensive knowledge of underlying architectural design decisions and inadvertently degrade the quality of the architecture while performing maintenance activities. In practice, this problem can be addressed through preserving the relationships between the requirements, architectural design decisions and their implementations in the source code, and then using this information to keep developers aware of critical architectural aspects of the code. This dissertation presents a novel approach that utilizes machine learning techniques to recover and preserve the relationships between architecturally significant requirements, architectural decisions and their realizations in the implemented code. Our approach for recovering architectural decisions includes the two primary stages of training and classification. In the first stage, the classifier is trained using code snippets of different architectural decisions collected from various software systems. During this phase, the classifier learns the terms that developers typically use to implement each architectural decision. These ``indicator terms\u27\u27 represent method names, variable names, comments, or the development APIs that developers inevitably use to implement various architectural decisions. A probabilistic weight is then computed for each potential indicator term with respect to each type of architectural decision. The weight estimates how strongly an indicator term represents a specific architectural tactics/decisions. For example, a term such as \emph{pulse} is highly representative of the heartbeat tactic but occurs infrequently in the authentication. After learning the indicator terms, the classifier can compute the likelihood that any given source file implements a specific architectural decision. The classifier was evaluated through several different experiments including classical cross-validation over code snippets of 50 open source projects and on the entire source code of a large scale software system. Results showed that classifier can reliably recognize a wide range of architectural decisions. The technique introduced in this dissertation is used to develop the Archie tool suite. Archie is a plug-in for Eclipse and is designed to detect wide range of architectural design decisions in the code and to protect them from potential degradation during maintenance activities. It has several features for performing change impact analysis of architectural concerns at both the code and design level and proactively keep developers informed of underlying architectural decisions during maintenance activities. Archie is at the stage of technology transfer at the US Department of Homeland Security where it is purely used to detect and monitor security choices. Furthermore, this outcome is integrated into the Department of Homeland Security\u27s Software Assurance Market Place (SWAMP) to advance research and development of secure software systems