Aldo Dagnino

Deriving Goals from a Use-Case Based Requirements Specification

Use cases and scenarios have emerged as prominent analysis tools during requirements engineering activities due to both their richness and informality. In some instances, for example when a project’s budget or schedule time is reduced at short notice, practitioners have been known to adopt a collection of use cases as a suitable substitute for a requirements specification. Given the challenges inherent in managing large collections of scenarios, this shortcut is cause for concern and deserves focused attention. We describe our experiences during a goal-driven requirements analysis effort for an electronic commerce application. In particular, we identify the specific risks incurred, focusing more on the challenges imposed due to traceability, inconsistent use of terminology, incompleteness and consistency, rather than on traditional software project management risks. We conclude by discussing the impact of the lessons learned for requirements engineering in the context of building quality systems during goal and scenario analysis.

Evolving beyond requirements creep: a risk-based evolutionary prototyping model

Evolutionary prototyping focuses on gathering a correct and consistent set of requirements. The process lends particular strength to building quality software by means of the ongoing clarification of existing requirements and the discovery of previously missing or unknown requirements. Traditionally, the iterative reexamination of a systems requirements has not been the panacea that practitioners sought, due to the predisposition for requirements creep and the difficulty in managing it. The paper proposes the combination of evolutionary prototyping and an aggressive risk mitigation strategy. Together, these techniques support successful requirements discovery and clarification, and they guard against the negative effects of requirements creep. We embody these techniques in a comprehensive software development model, which we call the EPRAM (Evolutionary Prototyping with Risk Analysis and Mitigation) model. The model was intentionally designed to comply with the Level 2 Key Process Area of the Software Engineering Institutes Capability Maturity Model. Validation is currently underway on several software development efforts that employ the model to support the rapid development of electronic commerce applications.

Human sensing for smart cities

Smart cities are powered by the ability to self-monitor and respond to signals and data feeds from heterogeneous physical sensors. These physical sensors, however, are fraught with interoperability and dependability challenges. Moreover, they also cannot shed light on human emotions and factors that impact smart city initiatives. Yet everyday, millions of city dwellers share their observations, thoughts, feelings, and experiences about their city through social media updates. This paper describes how citizens can serve as human sensors in providing supplementary, alternate, and complementary sources of information for smart cities. It presents a methodology, based on a probabilistic language model, to extract the perceptions that may be relevant to smart city initiatives from social media updates. Geo-tagged tweets collected over a two-month period from New York City are used to illustrate the potential of social media powered human sensors.

Coordination of hardware manufacturing and software development lifecycles for integrated systems development

With the introduction of the Industrial/sup IT/ vision at ABB, the coordination of hardware and software development is a crucial aspect that the organization needs to address. Software development and hardware production processes need to be coordinated into one coherent lifecycle model to optimize development. This paper presents some basic concepts in software and hardware development lifecycles and paves the road for a more comprehensive approach for a system development lifecycle.

Architectural requirements prioritization and analysis applied to software technology evaluation

In this short paper, we summarize an industrial project in which we developed and applied the Attribute Hierarchy-based Evaluation of Architectural Designs (AHEAD) method for selecting a software technology to form the basis for the next-generation architecture of a complex commercial software application. AHEAD leverages the Software Engineering Institutes Attribute-Driven Design (ADD) method and the Analytic Hierarchy Process (AHP) for evaluating software technologies that have important architectural impact. The core activities of AHEAD include elicitation, prioritization, and analysis of architectural requirements. The goal of these requirements activities was to establish and apply objective criteria for selecting, prototyping, and evaluating software technology alternatives. We found that using AHEAD brought greater objectivity to prioritization of architectural requirements and to the technical judgments of the software technology options.

A model to evaluate the economic benefits of software components development

ABB is a multi-national corporation that is developing a new generation of products based on the concept of Industrial/sup IT/. This concept provides a common integration platform for product interoperability. As Industrial/sup IT/ enabled products are developed across ABB, software reuse must be considered. Component based software development (CBSD) is an effective means to improve productivity and quality by developing reusable components. Measuring the economic benefits and performing sensitivity analyses of CBSD scenarios in the development of Industrial/sup IT/ products is important to improve efficiency. This paper presents a model that allows project leaders to evaluate a variety of software development scenarios. The model is based on a goal-question-metrics (GQM) approach and was developed at the ABB corporate research laboratories.

Code drones

We propose and explore a new paradigm called Code Drones in which every software artifact such as a class is an intelligent and socially active entity. In this paradigm, humanized artifacts take the lead and choreograph (socially, in collaboration with other intelligent software artifacts and humans) automated software engineering solutions to a myriad of development and maintenance challenges,including API migration, reuse, documentation, testing, patching, and refactoring. We discuss the implications of having social and intelligent/cognitive software artifacts that guide their own self-improvement.

Social media enabled human sensing for smart cities

Smart city initiatives rely on real-time measurements and data collected by a large number of heterogenous physical sensors deployed throughout a city. Physical sensors, however, are fraught with interoperability, dependability, management, and political challenges. Furthermore, these sensors are unable to sense the opinions and emotional reactions of citizens that invariably impact smart city initiatives. Yet everyday, millions of dwellers and visitors of a city share their observations, thoughts, feelings, and experiences, or in other words, their perceptions about their city through social media updates. This paper reasons why “human sensors”, namely, citizens that share information about their surroundings via social media can supplement, complement, or even replace the information measured by physical sensors. We present a methodology based on probabilistic language modeling to extract and visualize such perceptions that may be relevant to smart cities from social media updates. Using more than six million geo-tagged tweets collected over regions that feature widely varying geographical, social, cultural, and political characteristics and tweet densities, we illustrate the potential of social media enabled human sensing to address diverse smart city challenges.

Speeding up processing data from millions of smart meters

As an important element of the Smart Grid, Advanced Metering Infrastructure (AMI) systems have been implemented and deployed throughout the world in the past several years. An AMI system connects millions of end devices (e.g., smart meters and sensors in the residential level) with utility control centers via an efficient two-way communication infrastructure. AMI systems are able to exchange substantial meter data and control information between utilities and end devices in real-time or near real-time. The major challenge our research was to scale ABBs Meter Data Management System (MDMS) to manage data that originates from millions of smart meters. We designed a lightweight architecture capable of collect ever-increasing large amount of meter data from various metering systems, clean, analyze, and aggregate the meter data to support various smart grid applications. To meet critical high performance requirements, various concurrency processing techniques were implemented and integrated in our prototype. Our experiments showed that on average the implemented data file parser took about 42 minutes to complete parsing, cleaning, and aggregating 5.184 billion meter reads on a single machine with the hardware configuration of 12-core CPU, 32G RAM, and SSD Hard Drives. The throughput is about 7.38 billion meter reads (206.7GB data) per hour (i.e., 1811TB/year). In addition, well-designed publish/subscribe and communication infrastructures ensure the scalability and flexibility of the system.

Data Analytics for Power Utility Storm Planning

As the world population grows, recent climatic changes seem to bring powerful storms to populated areas. The impact of these storms on utility services is devastating. Hurricane Sandy is a recent example of the enormous damages that storms can inflict on infrastructure, society, and the economy. Quick response to these emergencies represents a big challenge to electric power utilities. Traditionally utilities develop preparedness plans for storm emergency situations based on the experience of utility experts and with limited use of historical data. With the advent of the Smart Grid, utilities are incorporating automation and sensing technologies in their grids and operation systems. This greatly increases the amount of data collected during normal and storm conditions. These data, when complemented with data from weather stations, storm forecasting systems, and online social media, can be used in analyses for enhancing storm preparedness for utilities. This paper presents a data analytics approach that uses real-world historical data to help utilities in storm damage projection. Preliminary results from the analysis are also included.