The Big Data Interagency Working Group (BD IWG) works to facilitate and further the goals of the White House Big Data R&D Initiative.
The CPS IWG is to coordinate programs, budgets, and policy recommendations for Cyber Physical Systems (CPS) research and development (R&D).
Cyber Security and Information Assurance (CSIA) Interagency Working Group coordinates the activities of the CSIA Program Component Area.
The Health Information Technology Research and Development Interagency Working Group coordinates programs, budgets and policy recommendations for Health IT R&D.
HCI&IM focuses on information interaction, integration, and management research to develop and measure the performance of new technologies.
HCSS R&D supports development of scientific foundations and enabling software and hardware technologies for the engineering, verification and validation, assurance, and certification of complex, networked, distributed computing systems and cyber-physical systems (CPS).
The HEC IWG coordinates the activities of the High End Computing (HEC) Infrastructure and Applications (I&A) and HEC Research and Development (R&D) Program Component Areas (PCAs).
LSN members coordinate Federal agency networking R&D in leading-edge networking technologies, services, and enhanced performance.
The purpose of the SPSQ IWG is to coordinate the R&D efforts across agencies that transform the frontiers of software science and engineering and to identify R&D areas in need of development that span the science and the technology of software creation and sustainment.
Formed to ensure and maximize successful coordination and collaboration across the Federal government in the important and growing area of video and image analytics
The Wireless Spectrum R&D (WSRD) Interagency Working Group (IWG) has been formed to coordinate spectrum-related research and development activities across the Federal government.
You can’t even ask them to push a button: Toward ubiquitous, developer-centric, empirical software engineering
Philip Johnson Department of Information and Computer Sciences University of Hawaii
October 31, 2001
Collection and analysis of empirical software project data is central to modern techniques for improving software quality, programmer productivity, and the economics of software project development. Unfortunately, barrierssurrounding the cost, quality, and utility of empirical project data hamper effective collection and application in many software development organizations.
This paper describes Hackystat, an approach to enabling ubiquitous collection and analysis of empirical software project data. The approach rests on three design criteria: data collection and analysis must be developer-centric rather than management-centric; it must be in-process rather than between-process, and it must be non-disruptive—it must not require developers to interrupt their activities to collect and/or analyze data. Hackystat is being implemented via an open source, sensor and web service based architecture. After a developer instruments their commercial development environment tools (such as their compiler, editor, version control system, and so forth) with Hackystat sensors, data is silently and unobtrusively collected and sent to a centralized web service. The web service runs analysis mechanisms over the data and sends email notiﬁcations back to a developer when “interesting” changes in their process or product occur.
Our research so far has yielded an initial operational release in daily use with a small set of sensors and analysis mechanisms, and a research agenda for expansion in the tools, the sensor data types, and the analyses. Our research has also identiﬁed several critical technical and social barriers, including: the ﬁdelity of the sensors; the coverage of the sensors; the APIs exposed by commercial tools for instrumentation; and the security and privacy considerations required to avoid adoption problems due to the spectre of “Big Brother”.
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