James A. Rodger

A probabilistic model for predicting software development effort

Recently, Bayesian probabilistic models have been used for predicting software development effort. One of the reasons for the interest in the use of Bayesian probabilistic models, when compared to traditional point forecast estimation models, is that Bayesian models provide tools for risk estimation and allow decision-makers to combine historical data with subjective expert estimates. In this paper, we use a Bayesian network model and illustrate how a belief updating procedure can be used to incorporate decision-making risks. We develop a causal model from the literature and, using a data set of 33 real-world software projects, we illustrate how decision-making risks can be incorporated in the Bayesian networks. We compare the predictive performance of the Bayesian model with popular nonparametric neural-network and regression tree forecasting models and show that the Bayesian model is a competitive model for forecasting software development effort.

A fuzzy nearest neighbor neural network statistical model for predicting demand for natural gas and energy cost savings in public buildings

This paper addresses the problem of predicting demand for natural gas for the purpose of realizing energy cost savings. Daily monitoring of a rooftop unit wireless sensor system provided feedback for a decision support system that supplied the demand for the required number of million cubic feet of natural gas used to control heating, ventilation, and air conditioning systems. The system was modeled with artificial neural networks (ANNs). Data on the consumption of the system were collected for 111days beginning September 21, 2012. The input/output data were used to train the ANN. The ANN approximated the data very well, showing that it can be used to predict demand for natural gas. A fuzzy nearest neighbor neural network statistical model consisting of four components was used. The predictive models were implemented by comparing regression, fuzzy logic, nearest neighbor, and neural networks. In addition, to optimize natural gas demand, we used the fuzzy regression nearest neighbor ANN model cost function to investigate the variables of price, operating expenses, cost to drill new wells, cost to turn gas on, oil price and royalties.

Toward reducing failure risk in an integrated vehicle health maintenance system

Highlights? Factor analysis identified four sub-systems: gear, engine, fuel and electrical. ? Fuzzy multi-sensor data fusion Kalman model developed. ? Fault detection and risk reduction in maintenance decision support system. ? Fuzzy Kalman filter approach reduced time and improved control of systems. This paper reports on a new integrated vehicle health maintenance system (IVHMS) based on fault detection and feedback. A fuzzy multi-sensor data fusion Kalman model was used to help reduce IVHMS failure risk. The IVHMS was tested, and sensors with and without faults were identified. The results demonstrate that multi-sensor data fusion based on fault detection and fuzzy Kalman feedback is an effective method of reducing risk in an IVHMS. Use of the fuzzy Kalman filter approach reduced the time needed to perform complex matrix manipulations to control higher order systems in the IVHMS. Moreover, the approach was able to capture the nonlinearity of engine operations under the influence of various anomalies.

A BPR case study at Honeywell

We embarked on a case study to explore one organization’s experiences with radical change for the purpose of uncovering how they achieved success. The organization we examined was Honeywell Inc. in Phoenix, Arizona, USA. From the interview data, we were able to devise a set of ten lessons to help others transform successfully. Two important lessons stand out above the rest. First, execution of a carefully developed change plan separates the high performers from less successful BPR projects. Second, recognition that dealing with change is difficult and complicated is not enough. Top management should make change management a top priority and communicate the change vision across the organization.

An empirical study of the impact of team size on software development effort

In this paper, we investigate the impact of team size on the software development effort. Using field data of over 200 software projects from various industries, we empirically test the impact of team size and other variables—such as software size in function points, ICASE tool and programming language type—on software development effort. Our results indicate that software size in function points significantly impacts the software development effort. The two-way interactions between function points and use of ICASE tool, and function points and language type are significant as well. Additionally, the interactions between team size and programming language type, and team size and use of ICASE tool were all significant.

An empirical study of the Cobb-Douglas production function properties of software development effort

In this paper we study whether software development effort exhibits Cobb-Douglas functional form with respect to team size and software size. We empirically test this relationship using real-world software engineering data set containing over 500 software projects. The results of our experiments indicate that the hypothesized Cobb-Douglas function form for software development effort with respect to team size and software size is true. We also find increasing returns to scale relationship between software size and team size with software development effort.

Application of a Fuzzy Feasibility Bayesian Probabilistic Estimation of supply chain backorder aging, unfilled backorders, and customer wait time using stochastic simulation with Markov blankets

Because supply chains are complex systems prone to uncertainty, statistical analysis is a useful tool for capturing their dynamics. Using data on acquisition history and data from case study reports, we used regression analysis to predict backorder aging using National Item Identification Numbers (NIINs) as unique identifiers. More than 56,000 NIINs were identified and used in the analysis. Bayesian analysis was then used to further investigate the NIIN component variables. The results indicated that it is statistically feasible to predict whether an individual NIIN has the propensity to become a backordered item. This paper describes the structure of a Bayesian network from a real-world supply chain data set and then determines a posterior probability distribution for backorders using a stochastic simulation based on Markov blankets. Fuzzy clustering was used to produce a funnel diagram that demonstrates that the Acquisition Advice Code, Acquisition Method Suffix Code, Acquisition Method Code, and Controlled Inventory Item Code backorder performance metric of a trigger group dimension may change dramatically with variations in administrative lead time, production lead time, unit price, quantity ordered, and stock. Triggers must be updated regularly and smoothly to keep up with the changing state of the supply chain backorder trigger clusters of market sensitiveness, collaborative process integration, information drivers, and flexibility.

A field study of the impact of gender and user's technical experience on the performance of voice-activated medical tracking application

Abstract Speech recognition is a particularly important technology for mobile computing since it provides a smaller, lighter interface than a keyboard. This paper investigates the impact of users gender and users computer experience on the performance of a speech recognition system. Using a field study of 33 users, voice-activated medical tracking application and a mobile healthcare fieldwork environment, we illustrate that the users gender, users computer experience and the interaction between the users gender and computer experience has an impact on the performance of a speech recognition system.

A fuzzy linguistic ontology payoff method for aerospace real options valuation

In this paper, we present a fuzzy linguistic ontology payoff method for the valuation of real options in the aerospace industry. Using real data, we apply the fuzzy linguistic approach to determine the credibility measures and the credibilistic expected value for the fuzzy real options valuation payoff method. This approach is used to obtain a multi-scenario modeling process by envisioning three scenarios: optimistic, most likely, and pessimistic. In addition, our experience with the scenario estimates is premised on results in an operating profit forecast. This forecast corresponds to a plausible outcome within the aerospace licensing maintenance, repair, and overhaul market and provides a decision-making tool. This tool can be utilized for determining real options for project valuation of aerospace licensing revenues based on unit costs, recurring costs, and quantity of units sold.

Development and initial testing of a theoretical model of transformation

We develop a holistic model of transformation. Model development is grounded in current transformation theory. We embarked on this daunting task because we believe that holism offers an excellent paradigm for managing change that can be of benefit to both academics and practitioners. We initially test our theoretical model by examining how well it maps onto an existing organization. The organization in question is heavily involved in enterprise transformation. It is a multi-billion dollar organization that is considered to be the top performer in its industry. Our general research question is: how do organizations successfully manage transformation?.