Ricardo Valerdi

Enterprise Transformation: Why Are We Interested, What Is It, and What Are the Challenges?

The concept of enterprise transformation has become increasingly popular as companies recognize the need to achieve an integrated perspective within and across organizational boundaries to address complex challenges. Yet, there is little clarity concerning what constitutes an “enterprise” or indeed “enterprise transformation.” This article is conceived as an initial step along the journey towards this clarity. There is considerable work to be done in delineating this area of interest and this article is offered as a stimulus for debate on what constitutes enterprise transformation. Drawing on themes from the management and systems engineering disciplines, the article will propose four characteristics of “enterprise” as a unit for transformation and look at why this holistic unit of analysis has become critical to businesses. The article will also ask what constitutes transformation, and offer characterizing criteria to distinguish this magnitude of change from more incremental changes. A recent empirical case study will be examined to further elucidate challenges faced in defining, leading, and transforming multi-organizational enterprises. Finally, a near-term research agenda is outlined for the evolving discipline of enterprise transformation.

Achievements and Challenges in Cocomo-Based Software Resource Estimation

This article summarizes major achievements and challenges of software resource estimation over the last 40 years, emphasizing the Cocomo suite of models. Critical issues that have enabled major achievements include the development of good model forms, criteria for evaluating models, methods for integrating expert judgment and statistical data analysis, and processes for developing new models that cover new software development approaches. The article also projects future trends in software development and evolution processes, along with their implications and challenges for future software resource estimation capabilities.

Guest EditorialIntegrated Healthcare Information Systems

The use of integrated information systems for healthcare has been started more than a decade ago. In recent years, rapid advances in information integration methods have spurred tremendous growth in the use of integrated information systems in healthcare delivery. Various techniques have been used for probing such integrated systems. These techniques include service-oriented architecture (SOA), EAI, workflow management, grid computing, and others. Many applications require a combination of these techniques, which gives rise to the emergence of enterprise systems in healthcare. Development of the techniques originated from different disciplines has the potential to significantly improve the performance of enterprise systems in healthcare. This editorial paper briefly introduces the enterprise systems in the perspective of healthcare informatics.

Guest Editorial Special Section on Enterprise Systems

The six papers in this special section are devoted to the topic of enterprise systems (ES) or enterprise information systems (EIS). ES has emerged as a promising tool used for integrating and extending business processes across the boundaries of business functions at both intra and interorganizational levels.

Heuristics for Systems Engineering Cost Estimation

Engineering cannot wait until all phenomena are explained. Engineers may work effectively, often for centuries, with heuristics. This paper provides thirty one heuristics that have been inspired by the development and application of a systems engineering cost estimation model. The objective of this paper is to present such heuristics in a simple manner so that they can benefit systems engineering researchers and practitioners that develop, calibrate, and use cost models.

Synthesizing SoS concepts for use in cost estimation

Todays need for more complex, capable systems in a short timeframe is leading many organizations towards the integration of existing systems into network-centric, knowledge-based system-of-systems (SoS). Software and system cost model tools to date have focused on the software and system development activities of a single system. When viewing the new SoS architectures, one finds that the effort associated with the design and integration of these SoSs is not handled well, if at all, in current cost models. This paper includes (I) a comparison of various SoS definitions and concepts with respect to cost models, (2) a classification of these definitions in terms of product, process, and personnel focus, and (3) the definition of a set of discriminators for defining model boundaries and potential drivers for an SoS cost estimation model. Eleven SoS definitions are synthesized to provide reasonable coverage for different properties of SoS and illustrated in two examples.

An empirical study of eServices product UML sizing metrics

Size is one of the most fundamental measurements of software. For the past two decades, the source line of code (SLOC) and function point (FP) metrics have been dominating software sizing approaches. However both approaches have significant defects. For example, SLOC can only be counted when the software construction is complete, while the FP counting is time consuming, expensive, and subjective. In the late 1990s researchers have been exploring faster, cheaper, and more effective sizing methods, such as Unified Modeling Language (UML) based software sizing. We present an empirical 14-project-study of three different sizing metrics which cover different software life-cycle activities: requirement metrics (requirement), UML metrics (architecture), and SLOC metrics (implementation). Our results show that the software size in terms of SLOC was moderately well correlated with the number of external use cases and the number of classes. We also demonstrate that the number of sequence diagram steps per external use case is a possible complexity indicator of software size. However, we conclude that at least for this 14-project eServices applications sample, the UML-based metrics were insufficiently well-defined and codified to serve as precise sizing metrics.

Analyzing and handling local bias for calibrating parametric cost estimation models

ContextParametric cost estimation models need to be continuously calibrated and improved to assure more accurate software estimates and reflect changing software development contexts. Local calibration by tuning a subset of model parameters is a frequent practice when software organizations adopt parametric estimation models to increase model usability and accuracy. However, there is a lack of understanding about the cumulative effects of such local calibration practices on the evolution of general parametric models over time. ObjectiveThis study aims at quantitatively analyzing and effectively handling local bias associated with historical cross-company data, thus improves the usability of cross-company datasets for calibrating and maintaining parametric estimation models. MethodWe design and conduct three empirical studies to measure, analyze and address local bias in cross-company dataset, including: (1) defining a method for measuring the local bias associated with individual organization data subset in the overall dataset; (2) analyzing the impacts of local bias on the performance of an estimation model; (3) proposing a weighted sampling approach to handle local bias. The studies are conducted on the latest COCOMO II calibration dataset. ResultsOur results show that the local bias largely exists in cross company dataset, and the local bias negatively impacts the performance of parametric model. The local bias based weighted sampling technique helps reduce negative impacts of local bias on model performance. ConclusionLocal bias in cross-company data does harm model calibration and adds noisy factors to model maintenance. The proposed local bias measure offers a means to quantify degree of local bias associated with a cross-company dataset, and assess its influence on parametric model performance. The local bias based weighted sampling technique can be applied to trade-off and mitigate potential risk of significant local bias, which limits the usability of cross-company data for general parametric model calibration and maintenance.

Cognitive Limits of Software Cost Estimation

This paper explores the cognitive limits of estimation in the context of software cost estimation. Two heuristics, representativeness and anchoring, motivate two experiments involving psychology students, engineering students, and engineering practitioners. The first experiment, designed to determine if there is a difference in estimating ability in everyday quantities, demonstrates that the three populations estimate with relatively equal accuracy. The results shed light on the distribution of estimates and the process of subjective judgment. The second experiment, designed to explore abilities for estimating the cost of software-intensive systems given incomplete information, shows that predictions by engineering students and practitioners are within 3-12% of each other. The value of this work is in helping better understand how software engineers make decisions based on limited information. The manifestation of the two heuristics is discussed together with the implications for the development of software cost estimation models in light of the findings from the two experiments.

Cost Metrics for Unmanned Aerial Vehicles

This paper aims to enhance the understanding of UAV cost metrics and their uses. The paper is organized into three main areas: (1) overview of current approaches for aircraft, (2) life cycle issues with UAV cost estimation, and (3) cost metrics and model approach as applied to UAVs. As a result of this work we hope to provide a better understanding of the cost factors influencing the recently publicized scrutiny of UAV cost overruns. More importantly, we hope to begin the foundation for the development of Cost Estimating Relationships (CERs) that can potentially lead to the development of a parametric cost model for UAVs.