306,598 research outputs found
Developing a global risk engine
Risk analysis is a critical link in the reduction of casualties and damages due to earthquakes. Recognition of this relation has led to a rapid rise in demand for accurate, reliable and flexible risk assessment software. However, there is a significant disparity between the high quality scientific data developed by researchers and the availability of versatile, open and user-friendly risk analysis tools to meet the demands of end-users. In the past few years several open-source software have been developed that play an important role in the seismic research, such as OpenSHA and OpenSEES. There is however still a gap when it comes to open-source risk assessment tools and software. In order to fill this gap, the Global Earthquake Model (GEM) has been created. GEM is an internationally sanctioned program initiated by the OECD that aims to build independent, open standards to calculate and communicate earthquake risk around the world. This initiative started with a one-year pilot project named GEM1, during which an evaluation of a number of existing risk software was carried out. After a critical review of the results it was concluded that none of the software were adequate for GEM requirements and therefore, a new object-oriented tool was to be developed. This paper presents a summary of some of the most well known applications used in risk analysis, highlighting the main aspects that were considered for the development of this risk platform. The research that was carried out in order to gather all of the necessary information to build this tool was distributed in four different areas: information technology approach, seismic hazard resources, vulnerability assessment methodologies and sources of exposure data. The main aspects and findings for each of these areas will be presented as well as how these features were incorporated in the up-to-date risk engine. Currently, the risk engine is capable of predicting human or economical losses worldwide considering both deterministic and probabilistic-based events, using vulnerability curves.
A first version of GEM will become available at the end of 2013. Until then the risk engine will continue to be developed by a growing community of developers, using a dedicated open-source platform
Risk management framework in Agile software development methodology
In software projects that use the Agile methodology, the focus is on development in small iterations to allow both frequent changes and client involvement. This methodology affects the risks that may happen in Agile software projects. Hence, these projects need a clear risk management process to reduce risks and address the problems before they arise. Most software production methodologies must use a framework for risk management, but currently, there is no such framework for the Agile methodology. Therefore, we present a risk management framework for projects that use the Agile methodology to help the software development process and increase the likelihood of the project’s success. The proposed framework states the necessary measures for risk management according to the ISO31000 standard at each stage of the Agile methodology. We evaluated the proposed framework in two running software projects with an Agile methodology by a number of expert experts. The results show that using our proposed framework increases the average positive risk reaction score by 49%
Iterative criteria-based approach to engineering the requirements of software development methodologies
Software engineering endeavours are typically based on and governed by the requirements of the target software; requirements identification is therefore an integral part of software development methodologies. Similarly, engineering a software development methodology (SDM) involves the identification of the requirements of the target methodology. Methodology engineering approaches pay special attention to this issue; however, they make little use of existing methodologies as sources of insight into methodology requirements. The authors propose an iterative method for eliciting and specifying the requirements of a SDM using existing methodologies as supplementary resources. The method is performed as the analysis phase of a methodology engineering process aimed at the ultimate design and implementation of a target methodology. An initial set of requirements is first identified through analysing the characteristics of the development situation at hand and/or via delineating the general features desirable in the target methodology. These initial requirements are used as evaluation criteria; refined through iterative application to a select set of relevant methodologies. The finalised criteria highlight the qualities that the target methodology is expected to possess, and are therefore used as a basis for de. ning the final set of requirements. In an example, the authors demonstrate how the proposed elicitation process can be used for identifying the requirements of a general object-oriented SDM. Owing to its basis in knowledge gained from existing methodologies and practices, the proposed method can help methodology engineers produce a set of requirements that is not only more complete in span, but also more concrete and rigorous
Addressing challenges to teach traditional and agile project management in academia
In order to prepare students for a professional IT career, most universities attempt to provide a current
educational curriculum in the Project Management (PM) area to their students. This is usually based on
the most promising methodologies used by the software industry. As instructors, we need to balance
traditional methodologies focused on proven project planning and control processes leveraging widely
accepted methods and tools along with the newer agile methodologies. Such new frameworks
emphasize that software delivery should be done in a flexible and iterative manner and with significant
collaboration with product owners and customers. In our experience agile methodologies have
witnessed an exponential growth in many diverse software organizations, and the various agile PM tools
and techniques will continue to see an increase in adoption in the software development sector.
Reflecting on these changes, there is a critical need to accommodate best practices and current methodologies in our courses that deliver Project Management content. In this paper we analyse two of the most widely used methodologies for traditional and agile software development – the widely used
ISO/PMBOK standard provided by the Project Management Institute and the well-accepted Scrum
framework. We discuss how to overcome curriculum challenges and deliver a quality undergraduate PM
course for a Computer Science and Information systems curricula. Based on our teaching experience
in Europe and North America, we present a comprehensive comparison of the two approaches. Our research covers the main concepts, processes, and roles associated with the two PM frameworks and recommended learning outcomes. The paper should be of value to instructors who are keen to see their computing students graduate with a sound understanding of current PM methodologies and who can deliver real-world software products.Accepted manuscrip
<i>Trace++</i>: A Traceability Approach for Agile Software Engineering
Agile methodologies have been introduced as an alternative to traditional software engineering methodologies. However, despite the advantages of using agile methodologies, the transition between traditional and agile methodologies is not an easy task. There are several problems associated with the use of agile methodologies. Examples of these problems are related to (i) lack of metrics to measure the amount of rework that occurs per sprint, (ii) interruption of a project after several iterations, (iii) changes in the requirements, (iv) lack of documentation, and (v) lack of management control. In this paper we present Trace++, a traceability technique that extends traditional traceability relationships with extra information in order to support the transition between traditional and agile software development. The use of Trace++ has been evaluated in two real projects of different software development companies to measure the benefits of using Trace++ to support agile software development
Comparative Study on Agile software development methodologies
Today-s business environment is very much dynamic, and organisations are
constantly changing their software requirements to adjust with new environment.
They also demand for fast delivery of software products as well as for
accepting changing requirements. In this aspect, traditional plan-driven
developments fail to meet up these requirements. Though traditional software
development methodologies, such as life cycle-based structured and object
oriented approaches, continue to dominate the systems development few decades
and much research has done in traditional methodologies, Agile software
development brings its own set of novel challenges that must be addressed to
satisfy the customer through early and continuous delivery of the valuable
software. It is a set of software development methods based on iterative and
incremental development process, where requirements and development evolve
through collaboration between self-organizing, cross-functional teams that
allows rapid delivery of high quality software to meet customer needs and also
accommodate changes in the requirements. In this paper, we significantly
identify and describe the major factors, that Agile development approach
improves software development process to meet the rapid changing business
environments. We also provide a brief comparison of agile development
methodologies with traditional systems development methodologies, and discuss
current state of adopting agile methodologies. We speculate that from the need
to satisfy the customer through early and continuous delivery of the valuable
software, Agile software development is emerged as an alternative to
traditional plan-based software development methods. The purpose of this paper,
is to provide an in-depth understanding, the major benefits of agile
development approach to software development industry, as well as provide a
comparison study report of ASDM over TSDM.Comment: 25 pages, 25 images, 86 references used, with authors biographie
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Designing a consulting services architecture model
textDuring my years of experience in the technology industry, it has become obvious that standard processes and methodologies within the engineering discipline are at a mature state. The realization though is that software engineering specifically lags behind. Most software engineering methodologies that I have studied focus on the mission of software development. It is this realization and the need for structure that led me to review existing methodologies used within my company's software services organization. The definition of what a successful software services methodology entails is rather limited. This report will provide a history of existing software engineering methodologies that I have studied, describe an initial services method that was being developed within my organization, develop a new model that addresses previous shortcomings and identify additional components required to further define a strong software services-oriented delivery methodology.Electrical and Computer Engineerin
Dynamic PRA: an Overview of New Algorithms to Generate, Analyze and Visualize Data
State of the art PRA methods, i.e. Dynamic PRA
(DPRA) methodologies, largely employ system
simulator codes to accurately model system dynamics.
Typically, these system simulator codes (e.g., RELAP5 )
are coupled with other codes (e.g., ADAPT,
RAVEN that monitor and control the simulation. The
latter codes, in particular, introduce both deterministic
(e.g., system control logic, operating procedures) and
stochastic (e.g., component failures, variable uncertainties)
elements into the simulation. A typical DPRA analysis is
performed by:
1. Sampling values of a set of parameters from the
uncertainty space of interest
2. Simulating the system behavior for that specific set of
parameter values
3. Analyzing the set of simulation runs
4. Visualizing the correlations between parameter values
and simulation outcome
Step 1 is typically performed by randomly sampling
from a given distribution (i.e., Monte-Carlo) or selecting
such parameter values as inputs from the user (i.e.,
Dynamic Event Tre
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