An Objectives-Driven Process for Selecting Methods to Support Requirements Engineering Activities

This paper presents a framework that guides the requirements engineer in the implementation and execution of an effective requirements generation process. We achieve this goal by providing a well-defined requirements engineering model and a criteria based process for optimizing method selection for attendant activities. Our model, unlike other models, addresses the complete requirements generation process and consists of activities defined at more adequate levels of abstraction. Additionally, activity objectives are identified and explicitly stated - not implied as in the current models. Activity objectives are crucial as they drive the selection of methods for each activity. Our model also incorporates a unique approach to verification and validation that enhances quality and reduces the cost of generating requirements. To assist in the selection of methods, we have mapped commonly used methods to activities based on their objectives. In addition, we have identified method selection criteria and prescribed a reduced set of methods that optimize these criteria for each activity defined by our requirements generation process. Thus, the defined approach assists in the task of selecting methods by using selection criteria to reduce a large collection of potential methods to a smaller, manageable set. The model and the set of methods, taken together, provide the much needed guidance for the effective implementation and execution of the requirements generation process.Comment: 20 pages, 5 figures, 3 tables, publisheed: 29th Annual IEEE/NASA Software Engineering Workshop, April 200

An Exploratory Study on the Strategic Use of Information Technology in the Source Selection Decision-Making Process

The strategic use of Information Technology in the acquisition field can be very useful in the decision making process of evaluating alternative solutions during a Government source selection. Current implementation of information technology provides a more tactical approach to systems development. The use of Electronic Commerce/Electronic Data Interchange and the internet to electronically transfer information is only the beginning of the shift towards a more strategic design process for information systems within Government procurement agencies. A schematic model was designed to demonstrate how information technology, such as Decision Support Systems, Expert Systems, and Shared Data Warehousing could assist the SSA in selecting the optimal, or best value solution. In addition, three source selection evaluation models using management science techniques were designed and developed using Microsoft Excel software. The Sealed Bidding, FAR Part 14, and Competitive Proposal, FAR Part 15 models implemented Integer Linear Programming through Microsoft Excel\u27s SOLVER option. The AFFARS Appendix AA/BB model implemented the use of the multi-criteria Analytical Hierarchy Process

A Model Selection Procedure for Stream Re-Aeration Coefficient Modelling

Model selection is finding wide applications in a lot of modelling and environmental problems. However, applications of model selection to re-aeration coefficient studies are still limited. The current study explores the use of model selection in re-aeration coefficient studies by combining several suggestions from numerous authors on the interpretation of data regarding re-aeration coefficient modelling. The model selection procedure applied in this research made use of Akaike information criteria, measures of agreement such as percent bias (PBIAS), Nash-Sutcliffe Efficiency (NSE) and root mean square error (RMSE) observation Standard deviation Ratio (RSR) and gragh analysis in selecting the best performing model. An algorithm prescribing a generic model selection procedure was also provided. Out of ten candidates models used in this study, the O’Connor and Dobbins (1958) model emerged as the top performing model in its application to data collected from River Atuwara in Nigeria. The suggested process could save software and model developers lots of time and resources, which would otherwise be spent in investigating and developing new models. The procedure is also ideal in selecting a model in situations where there is no overwhelming support for any particular model by observed data

Outsourcing and acquisition models comparison related to IT supplier selection decision analysis

This paper presents a comparison of acquisition models related to decision analysis of IT supplier selection. The main standards are: Capability Maturity Model Integration for Acquisition (CMMI-ACQ), ISO / IEC 12207 Information Technology / Software Life Cycle Processes, IEEE 1062 Recommended Practice for Software Acquisition, the IT Infrastructure Library (ITIL) and the Project Management Body of Knowledge (PMBOK) guide. The objective of this paper is to compare the previous models to find the advantages and disadvantages of them for the future development of a decision model for IT supplier selection

An artificial neural network for dimensions and cost modelling of internal micro-channels fabricated in PMMA using Nd:YVO4 laser

For micro-channel fabrication using laser micro-machining processing, estimation techniques are normally utilised to develop an approach for the system behaviour evaluation. Design of Experiments (DOE) and the Artificial Neural Networks (ANN) are two methodologies that can be used as estimation techniques. These techniques help in finding a set of laser processing parameters that provides the required micro-channel dimensions and in finding the optimal solutions in terms reducing the product development time, power consumption and of least cost. In this work, an integrated methodology is presented in which the ANN training experiments were obtained by the statistical software DoE to improve the developed models in ANN. A 33 factorial design of experiments (DoE) was used to get the experimental set. Laser power, P; pulse repetition frequency, PRF; and sample translation speed, U were the ANN inputs. The channel width and the produced micro-channel operating cost per metre were the measured responses. Four Artificial Neural Networks (ANNs) models were developed to be applied to internal micro-channels machined in PMMA using a Nd:YVO4 laser. These models were varied in terms of the selection and the quantity of training data set and constructed using a multi-layered, feed-forward structure with a the back-propagation algorithm. The responses were adequately estimated by the ANN models within the set micro-machining parameters limits. Moreover the effect of changing the selection and the quantity of training data on the approximation capability of the developed ANN model was discussed