Marginal cost-based pricing of distribution: a case study

This paper presents results of a software development project carried out by the “Electricity North West” (ENW) and “TNEI” to find economic use-of-system charges for the extra high-voltage (EHV) network. Several cost-based charging models which satisfy principles set by the Regulator, such as cost reflectivity, predictability, stability and transparency were developed. In this paper, the emphasis is put on the developed software and the comparison of nodal marginal charges obtained from the proposed pricing models

COSTMODL: An automated software development cost estimation tool

The cost of developing computer software continues to consume an increasing portion of many organizations' total budgets, both in the public and private sector. As this trend develops, the capability to produce reliable estimates of the effort and schedule required to develop a candidate software product takes on increasing importance. The COSTMODL program was developed to provide an in-house capability to perform development cost estimates for NASA software projects. COSTMODL is an automated software development cost estimation tool which incorporates five cost estimation algorithms including the latest models for the Ada language and incrementally developed products. The principal characteristic which sets COSTMODL apart from other software cost estimation programs is its capacity to be completely customized to a particular environment. The estimation equations can be recalibrated to reflect the programmer productivity characteristics demonstrated by the user's organization, and the set of significant factors which effect software development costs can be customized to reflect any unique properties of the user's development environment. Careful use of a capability such as COSTMODL can significantly reduce the risk of cost overruns and failed projects

An Investigation of Rule Induction Based Prediction Systems

Traditionally, researchers have used either off-the-shelf models such as COCOMO, or developed local models using statistical techniques such as stepwise regression, to predict software effort estimates. More recently, attention has turned to a variety of machine learning methods such as artificial neural networks (ANNs), case-based reasoning (CBR) and rule induction (RI). This position paper outlines some preliminary research into the use of rule induction methods to build software cost models. We briefly describe the use of rule induction methods and then apply the technique to a dataset of 81 software projects derived from a Canadian software house in the late 1980s. We show that RI methods tend to be unstable and generally predict with quite variable accuracy. Pruning the feature set, however, has a significant impact upon accuracy. We also compare our results with a prediction system based upon a standard regression procedure. We suggest that further work is carried out to examine the effects of the relationships among, and between, the features of the attributes on the generated rules in an attempt to improve on current prediction techniques and enhance our understanding of machine learning methods

An Optimal Control Model of Technology Transition

This paper discusses the use of optimization software to solve an optimal control problem arising in the modeling of technology transition. We set up a series of increasingly complex models with such features as learning-by-doing, adjustment cost, and capital investment. The models are written in continuous time and then discretized by using different methods to transform them into large-scale nonlinear programs. We use a modeling language and numerical optimization methods to solve the optimization problem. Our results are consistent with ndings in the literature and highlight the impact the discretization choice has on the solution and accuracy.

Software quality evaluation models applicable in health information and communications technologies: a review of the literature

Information and Communications Technologies in healthcare has increased the need to consider quality criteria through standardised processes. The aim of this study was to analyse the software quality evaluation models applicable to healthcare from the perspective of ICT-purchasers. Through a systematic literature review with the keywords software, product, quality, evaluation and health, we selected and analysed 20 original research papers published from 2005-2016 in health science and technology databases. The results showed four main topics: non- ISO models, software quality evaluation models based on ISO/IEC standards, studies analysing software quality evaluation models, and studies analysing ISO standards for software quality evaluation. The models provide cost-efficiency criteria for specific software, and improve use outcomes. The ISO/IEC25000 standard is shown as the most suitable for evaluating the quality of ICTs for healthcare use from the perspective of institutional acquisition

A Treeboost Model for Software Effort Estimation Based on Use Case Points

Software effort prediction is an important task in the software development life cycle. Many models including regression models, machine learning models, algorithmic models, expert judgment and estimation by analogy have been widely used to estimate software effort and cost. In this work, a Treeboost (Stochastic Gradient Boosting) model is put forward to predict software effort based on the Use Case Point method. The inputs of the model include software size in use case points, productivity and complexity. A multiple linear regression model was created and the Treeboost model was evaluated against the multiple linear regression model, as well as the use case point model by using four performance criteria: MMRE, PRED, MdMRE and MSE. Experiments show that the Treeboost model can be used with promising results to estimate software effort

RevBayes: Bayesian Phylogenetic Inference Using Graphical Models and an Interactive Model-Specification Language.

Programs for Bayesian inference of phylogeny currently implement a unique and fixed suite of models. Consequently, users of these software packages are simultaneously forced to use a number of programs for a given study, while also lacking the freedom to explore models that have not been implemented by the developers of those programs. We developed a new open-source software package, RevBayes, to address these problems. RevBayes is entirely based on probabilistic graphical models, a powerful generic framework for specifying and analyzing statistical models. Phylogenetic-graphical models can be specified interactively in RevBayes, piece by piece, using a new succinct and intuitive language called Rev. Rev is similar to the R language and the BUGS model-specification language, and should be easy to learn for most users. The strength of RevBayes is the simplicity with which one can design, specify, and implement new and complex models. Fortunately, this tremendous flexibility does not come at the cost of slower computation; as we demonstrate, RevBayes outperforms competing software for several standard analyses. Compared with other programs, RevBayes has fewer black-box elements. Users need to explicitly specify each part of the model and analysis. Although this explicitness may initially be unfamiliar, we are convinced that this transparency will improve understanding of phylogenetic models in our field. Moreover, it will motivate the search for improvements to existing methods by brazenly exposing the model choices that we make to critical scrutiny. RevBayes is freely available at http://www.RevBayes.com [Bayesian inference; Graphical models; MCMC; statistical phylogenetics.]