It's Not a Matter of Time: Highlights From the 2011 Competency-Based Learning Summit

Outlines discussions about the potential and challenges of competency-based learning in transforming the current time-based system, including issues of accountability, equity, personalization, and aligning policy and practice. Includes case summaries

Estimating, planning and managing Agile Web development projects under a value-based perspective

Context: The processes of estimating, planning and managing are crucial for software development projects, since the results must be related to several business strategies. The broad expansion of the Internet and the global and interconnected economy make Web development projects be often characterized by expressions like delivering as soon as possible, reducing time to market and adapting to undefined requirements. In this kind of environment, traditional methodologies based on predictive techniques sometimes do not offer very satisfactory results. The rise of Agile methodologies and practices has provided some useful tools that, combined with Web Engineering techniques, can help to establish a framework to estimate, manage and plan Web development projects. Objective: This paper presents a proposal for estimating, planning and managing Web projects, by combining some existing Agile techniques with Web Engineering principles, presenting them as an unified framework which uses the business value to guide the delivery of features. Method: The proposal is analyzed by means of a case study, including a real-life project, in order to obtain relevant conclusions. Results: The results achieved after using the framework in a development project are presented, including interesting results on project planning and estimation, as well as on team productivity throughout the project. Conclusion: It is concluded that the framework can be useful in order to better manage Web-based projects, through a continuous value-based estimation and management process.Ministerio de Economía y Competitividad TIN2013-46928-C3-3-

Data Driven Surrogate Based Optimization in the Problem Solving Environment WBCSim

Large scale, multidisciplinary, engineering designs are always difficult due to the complexity and dimensionality of these problems. Direct coupling between the analysis codes and the optimization routines can be prohibitively time consuming due to the complexity of the underlying simulation codes. One way of tackling this problem is by constructing computationally cheap(er) approximations of the expensive simulations, that mimic the behavior of the simulation model as closely as possible. This paper presents a data driven, surrogate based optimization algorithm that uses a trust region based sequential approximate optimization (SAO) framework and a statistical sampling approach based on design of experiment (DOE) arrays. The algorithm is implemented using techniques from two packages—SURFPACK and SHEPPACK that provide a collection of approximation algorithms to build the surrogates and three different DOE techniques—full factorial (FF), Latin hypercube sampling (LHS), and central composite design (CCD)—are used to train the surrogates. The results are compared with the optimization results obtained by directly coupling an optimizer with the simulation code. The biggest concern in using the SAO framework based on statistical sampling is the generation of the required database. As the number of design variables grows, the computational cost of generating the required database grows rapidly. A data driven approach is proposed to tackle this situation, where the trick is to run the expensive simulation if and only if a nearby data point does not exist in the cumulatively growing database. Over time the database matures and is enriched as more and more optimizations are performed. Results show that the proposed methodology dramatically reduces the total number of calls to the expensive simulation runs during the optimization process

Towards holistic goal centered performance management in software development: lessons from a best practice analysis

There are strong motivating factors for more effective performance measurement practices in software development. Astute practices in this domain are lauded to improve efficiency and effectiveness. However previous studies have shown that measurement in software is intricate, complex and fraught with challenges. Consequently it is poorly managed in practice. Our research seeks to better understand performance management in a real world software development setting in order to identify the challenges and generate a roadmap for improvement. This paper presents findings from an inductive analysis of a radical measurement program in a global software organization. Our study investigates the level at which non-compliance with best practice can explain the company’s disappointing results. We found that a narrow focus on projects, rather than on organizational goals, has seriously hindered its success. We also found that the rate of change in the organization as a whole was impinging on the effective implementation of its measurement program. An analysis of the results demonstrates just how challenging software measurement is. The findings provide an evaluation of best practice relative to the literature that is informed by real industry experience

Accessing Antecedents and Outcomes of RFID Implementation in Health Care

This research first conceptualizes, develops, and validates four constructs for studying RFID in health care, including Drivers (Internal and External), Implementation Level (Clinical Focus and Administrative Focus), Barriers (Cost Issues, Lack of Understanding, Technical Issues, and Privacy and Security Concerns), and Benefits (Patient Care, Productivity, Security and Safety, Asset Management, and Communication). Data for the study were collected from 88 health care organizations and the measurement scales were validated using structural equation modeling. Second, a framework is developed to discuss the causal relationships among the above mentioned constructs. It is found that Internal Drivers are positively related to Implementation Level, which in turn is positively related to Benefits and Performance. In addition, Barriers are found to be positively related to Implementation Level, which is in contrast to the originally proposed negative relationship. The research also compares perception differences regarding RFID implementation among the non-implementers, future implementers, and current implementers of RFID. It is found that both future implementers and current implementers consider RFID barriers to be lower and benefits to be higher compared to the non-implementers. This paper ends with our research implications, limitations and future research

Business modeling in process-oriented organizations for RUP-based software development

Several organizations are nowadays not particularly comfortable with their internal structuring based on a hierarchical arrangement (sub-divided in departments), where collaborators with a limited view of the overall organization perform their activities. Those organizations recognize the need to move to a model where multi-skilled teams run horizontal business processes that cross the organization, and impact suppliers and clients. To develop software systems for any organization, the development process must always be appropriate and controlled. Additionally for organizations who want to migrate to a horizontal business processes view, it is required to model the organizational platform where the organizational processes will run. This necessity is also true when the organization under consideration is a software house. In this chapter, a proposal of a generic framework for process-oriented software houses is presented. The way of managing the process model and the instantiation of their processes with the Rational Unified Process (RUP) disciplines, whenever they are available, or with other kind of processes is recommended as a way to control and define the software development process. To illustrate the usefulness of the proposal, it is presented how the generic reference framework was executed in a real project called “Premium Wage” and shown, in some detail, the created artifacts (which include several UML models) during the development phases following the RUP disciplines, especially the artifacts produced for business modeling.(undefined

A Prototype Toolkit For Evaluating Indoor Environmental Quality In Commercial Buildings

Measurement of building environmental parameters is often complex, expensive, and not easily proceduralized in a manner that covers all commercial buildings. Evaluating building indoor environmental quality performance is therefore not standard practice. This project developed a prototype toolkit that addressed existing barriers to widespread indoor environmental quality performance evaluation. A toolkit with both hardware and software elements was designed for practitioners around the indoor environmental quality requirements of the American Society of Heating, Refrigeration and Air Conditioning Engineers / Chartered Institution of Building Services / United States Green Building Council Performance Measurement Protocols. This unique toolkit was built on a wireless mesh network with a web-based data collection, analysis, and reporting application. The toolkit provided a fast, robust deployment of sensors, real-time data analysis, Performance Measurement Protocol-based analysis methods and a scorecard and report generation tools. A web-enabled Geographic Information System-based metadata collection system also reduced field-study deployment time. The toolkit was evaluated through three case studies, which were discussed in this report

AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap

The rise of powerful large language models (LLMs) brings about tremendous opportunities for innovation but also looming risks for individuals and society at large. We have reached a pivotal moment for ensuring that LLMs and LLM-infused applications are developed and deployed responsibly. However, a central pillar of responsible AI -- transparency -- is largely missing from the current discourse around LLMs. It is paramount to pursue new approaches to provide transparency for LLMs, and years of research at the intersection of AI and human-computer interaction (HCI) highlight that we must do so with a human-centered perspective: Transparency is fundamentally about supporting appropriate human understanding, and this understanding is sought by different stakeholders with different goals in different contexts. In this new era of LLMs, we must develop and design approaches to transparency by considering the needs of stakeholders in the emerging LLM ecosystem, the novel types of LLM-infused applications being built, and the new usage patterns and challenges around LLMs, all while building on lessons learned about how people process, interact with, and make use of information. We reflect on the unique challenges that arise in providing transparency for LLMs, along with lessons learned from HCI and responsible AI research that has taken a human-centered perspective on AI transparency. We then lay out four common approaches that the community has taken to achieve transparency -- model reporting, publishing evaluation results, providing explanations, and communicating uncertainty -- and call out open questions around how these approaches may or may not be applied to LLMs. We hope this provides a starting point for discussion and a useful roadmap for future research