Documentation Driven Software Development

The views, opinions and/or findings contained in this report are those of the author(s) and should not contrued as an official Department of the Army position, policy or decision, unless so designated by other documentation.Our objective is to develop an integrated, systematic, documentation centric approach to software development, known as Documentation Driven Software Development (DDD). The research issues for DDD are creation and application of three key documenting technologies that will drive the development process and a Document Management System (DMS) that will support them. These technologies address (1) representations for active documents; (2) representations for repositories; (3) methods for analysis, transformation, and presentation of this information. In addition, we explored new possibilities for computed-aided interfaces that help humans with routine tasks. In doing so we applied Cognitive Science and machine learning methods to design user interfaces that can learn and assist users. We also expanded our work in the area of integration of ontologies from heterogeneous sources. Specifically, we studied Knowledge System Integration Ontology (KSIO) that aligns data and information systems with current situational context for the efficient knowledge collection, integration and transfer. The role of ontology is to organize and structure knowledge (e.g. by standardized terminology) so that semantic queries and associations become more efficient. We assessed the degree to which natural language processing can be usefully applied to the analysis of requirement changes and their impact on system structure and implementation

Innovations for Requirements Analysis, From Stakeholders' Needs to Formal Designs

14th MontereyWorkshop 2007 Monterey, CA, USA, September 10-13, 2007 Revised Selected PapersWe are pleased to present the proceedings of the 14thMontereyWorkshop, which took place September 10–13, 2007 in Monterey, CA, USA. In this preface, we give the reader an overview of what took place at the workshop and introduce the contributions in this Lecture Notes in Computer Science volume. A complete introduction to the theme of the workshop, as well as to the history of the Monterey Workshop series, can be found in Luqi and Kordon’s “Advances in Requirements Engineering: Bridging the Gap between Stakeholders’ Needs and Formal Designs” in this volume. This paper also contains the case study that many participants used as a problem to frame their analyses, and a summary of the workshop’s results

Boot Camp for Cognitive Systems

The Defense Advanced Research Projects Agency (DARPA) has implemented a program to build the first instance of a complete cognitive agent. The program, called Personalized Assistant that Learns (PAL), is expected to yield new cognitive technology of significant value not only to the military, but also to the business and academic sectors. (Gunning 2004) With traditional engineering projects, evaluation can be done in a straightforward manner determining if the documented requirements of the system have been met. Agent-based capabilities and other network centric capabilities (e.g., web services) complicate matters because the environment that they will operate under constantly changes. Add to that complication, the ability to learn new capabilities, and testing whether or not a new agent is ready to be deployed becomes a problem beyond the current state of art and practice. In this paper an initial experiment design is discussed as well as a description of a broader approach for evaluation in transitioning cognitive systems that learn into an operational environment

PAL Boot Camp: Preparing Cognitive Assistants for Deployment

Most visions for decision support and information technology anticipate the use of machine learning to enable software to respond to an adapting environment, including the ability to learn capabilities while on-the-job. Currently, systems and software engineering processes hinder employment of task learning technology, because the adaptation it provides runs counter to our notions of stability. Similarly, systems must typically demonstrate satisfaction of requirements before deployment, rather than learn tasks while on the job. This paper introduces new problems for the field of software engineering and discusses an approach for preparing cognitive systems for deployment. We describe one approach to a boot camp for cognitive systems and present the results of simulations of the boot camp. The results of our experiments provide thresholds and patterns for knowledge, and the requirement for specific patterns of human use of cognitive systems. These results are then used to infer requirements for a boot camp and measures for the prediction of successful employment of the assistant.Space and Naval Warfare Systems Center,San Diego,CA,9215

Human and Software Factors for Successful System Adaptation

12th International Command and Control Research and Technology Symposium (ICCRTS), June 19-21, 2007 at the Naval War College, Newport, RI.Most visions for decision support and information technology anticipate the use of machine learning to enable software to respond to an adapting environment, including the ability to learn capabilities while on-the-job. Currently, systems and software engineering processes hinder employment of task learning technology, because the adaptation it provides runs counter to our notions of stability. Similarly, systems must typically demonstrate satisfaction of requirements before deployment, rather than learn tasks while on the job. This paper introduces new problems for the field of software engineering and discusses an approach for preparing cognitive systems for deployment. We describe one approach to a boot camp for cognitive systems and present the results of simulations of the boot camp. The results of our experiments provide thresholds and patterns for knowledge, and the requirement for specific patterns of human use of cognitive systems. These results are then used to infer requirements for a boot camp and measures for the prediction of successful employment of the assistant