A knowledge based software engineering environment testbed

The Carnegie Group Incorporated and Boeing Computer Services Company are developing a testbed which will provide a framework for integrating conventional software engineering tools with Artifical Intelligence (AI) tools to promote automation and productivity. The emphasis is on the transfer of AI technology to the software development process. Experiments relate to AI issues such as scaling up, inference, and knowledge representation. In its first year, the project has created a model of software development by representing software activities; developed a module representation formalism to specify the behavior and structure of software objects; integrated the model with the formalism to identify shared representation and inheritance mechanisms; demonstrated object programming by writing procedures and applying them to software objects; used data-directed and goal-directed reasoning to, respectively, infer the cause of bugs and evaluate the appropriateness of a configuration; and demonstrated knowledge-based graphics. Future plans include introduction of knowledge-based systems for rapid prototyping or rescheduling; natural language interfaces; blackboard architecture; and distributed processin

Machine learning research 1989-90

Multifunctional knowledge bases offer a significant advance in artificial intelligence because they can support numerous expert tasks within a domain. As a result they amortize the costs of building a knowledge base over multiple expert systems and they reduce the brittleness of each system. Due to the inevitable size and complexity of multifunctional knowledge bases, their construction and maintenance require knowledge engineering and acquisition tools that can automatically identify interactions between new and existing knowledge. Furthermore, their use requires software for accessing those portions of the knowledge base that coherently answer questions. Considerable progress was made in developing software for building and accessing multifunctional knowledge bases. A language was developed for representing knowledge, along with software tools for editing and displaying knowledge, a machine learning program for integrating new information into existing knowledge, and a question answering system for accessing the knowledge base

Knowledge-intensive software design systems: Can too much knowledge be a burden?

While acknowledging the considerable benefits of domain-specific, knowledge-intensive approaches to automated software engineering, it is prudent to carefully examine the costs of such approaches, as well. In adding domain knowledge to a system, a developer makes a commitment to understanding, representing, maintaining, and communicating that knowledge. This substantial overhead is not generally associated with domain-independent approaches. In this paper, I examine the downside of incorporating additional knowledge, and illustrate with examples based on our experience in building the SIGMA system. I also offer some guidelines for developers building domain-specific systems

Incorporating engineering in high school biology

textThe purpose of this project was to create a series of lessons that incorporate both Biology and Engineering concepts. The three lessons were intended to increase in complexity as the students progress throughout the year. Using PyMol software allowed students to visually represent complex protein structures while introducing and providing an opportunity to practice programming. Each lesson was followed by a worksheet or activity to aid in students' comprehension and application of practice. These lessons were designed to maximize students' time learning to program and using PyMol software while enhancing the current curriculum. Lesson one introduced students to the PyMol software while building and representing the four main structures of proteins. With increased programming knowledge, lesson two focused on modeling the DNA double helix. The final lesson introduced students to evolutionary relationships based on a protein's amino acid sequence.Science, Technology, Engineering, and Mathematics Educatio

An Ontology Model to Support the Automated Evaluation of Software

Even though previous research has tried to model Software Engineering knowledge, focusing either on the entire discipline or on parts of it, we lack an integrated conceptual model for representing software evaluations, and we also lack the information related to them that supports their definition and enables their automation and reproducibility. This paper presents an extensible ontology model for representing software evaluations and evaluation campaigns, i.e., worldwide activities where a group of tools is evaluated according to a certain evaluation specification using common test data. During the development of the ontologies, we have reused current standards and models and have linked these ontologies with some renowned ones

An Evidence-based Roadmap for IoT Software Systems Engineering

Context: The Internet of Things (IoT) has brought expectations for software inclusion in everyday objects. However, it has challenges and requires multidisciplinary technical knowledge involving different areas that should be combined to enable IoT software systems engineering. Goal: To present an evidence-based roadmap for IoT development to support developers in specifying, designing, and implementing IoT systems. Method: An iterative approach based on experimental studies to acquire evidence to define the IoT Roadmap. Next, the Systems Engineering Body of Knowledge life cycle was used to organize the roadmap and set temporal dimensions for IoT software systems engineering. Results: The studies revealed seven IoT Facets influencing IoT development. The IoT Roadmap comprises 117 items organized into 29 categories representing different concerns for each Facet. In addition, an experimental study was conducted observing a real case of a healthcare IoT project, indicating the roadmap applicability. Conclusions: The IoT Roadmap can be a feasible instrument to assist IoT software systems engineering because it can (a) support researchers and practitioners in understanding and characterizing the IoT and (b) provide a checklist to identify the applicable recommendations for engineering IoT software systems

OntoWebML: A Knowledge Base Management System for WSML Ontologies

This paper addresses the topic of defining a knowledge base system for representing and managing ontologies according to the WSMO conceptual model. We propose a software engineering approach to this problem, by implementing: (i) the relational model for ontologies that corresponds to the WSML representation of WSMO; (ii) the usage of a well known Web modeling language called WebML, extended by a set of new components for exploiting ontological contents in Web services and Web applications design; and (iii) a Web-based content management system for ontologies editing and reasoning, implemented using the abovementioned software engineering approach

Ontologies and Methods for Interoperability of Engineering Analysis Models (eams) in an E-Design Environment

ABSTRACT ONTOLOGIES AND METHODS FOR INTEROPERABILITY OF ENGINEERING ANALYSIS MODELS (EAMS) IN AN E-DESIGN ENVIRONMENT SEPTEMBER 2007 NEELIMA KANURI, B.S., BIRLA INSTITUTE OF TECHNOLOGY AND SCIENCES PILANI INDIA M.S., UNIVERSITY OF MASSACHUSETTS AMHERST Directed by: Professor Ian Grosse Interoperability is the ability of two or more systems to exchange and reuse information efficiently. This thesis presents new techniques for interoperating engineering tools using ontologies as the basis for representing, visualizing, reasoning about, and securely exchanging abstract engineering knowledge between software systems. The specific engineering domain that is the primary focus of this report is the modeling knowledge associated with the development of engineering analysis models (EAMs). This abstract modeling knowledge has been used to support integration of analysis and optimization tools in iSIGHT FD , a commercial engineering environment. ANSYS , a commercial FEA tool, has been wrapped as an analysis service available inside of iSIGHT-FD. Engineering analysis modeling (EAM) ontology has been developed and instantiated to form a knowledge base for representing analysis modeling knowledge. The instances of the knowledge base are the analysis models of real world applications. To illustrate how abstract modeling knowledge can be exploited for useful purposes, a cantilever I-Beam design optimization problem has been used as a test bed proof-of-concept application. Two distinct finite element models of the I-beam are available to analyze a given beam design- a beam-element finite element model with potentially lower accuracy but significantly reduced computational costs and a high fidelity, high cost, shell-element finite element model. The goal is to obtain an optimized I-beam design at minimum computational expense. An intelligent KB tool was developed and implemented in FiPER . This tool reasons about the modeling knowledge to intelligently shift between the beam and the shell element models during an optimization process to select the best analysis model for a given optimization design state. In addition to improved interoperability and design optimization, methods are developed and presented that demonstrate the ability to operate on ontological knowledge bases to perform important engineering tasks. One such method is the automatic technical report generation method which converts the modeling knowledge associated with an analysis model to a flat technical report. The second method is a secure knowledge sharing method which allocates permissions to portions of knowledge to control knowledge access and sharing. Both the methods acting together enable recipient specific fine grain controlled knowledge viewing and sharing in an engineering workflow integration environment, such as iSIGHT-FD. These methods together play a very efficient role in reducing the large scale inefficiencies existing in current product design and development cycles due to poor knowledge sharing and reuse between people and software engineering tools. This work is a significant advance in both understanding and application of integration of knowledge in a distributed engineering design framework

Domain and Specification Models for Software Engineering

This paper discusses our approach to representing application domain knowledge for specific software engineering tasks. Application domain knowledge is embodied in a domain model. Domain models are used to assist in the creation of specification models. Although many different specification models can be created from any particular domain model, each specification model is consistent and correct with respect to the domain model. One aspect of the system-hierarchical organization is described in detail

Comparison of knowledge representation in PDM and by semantic networks

\u27Nowadays, computer-aided tools have enabled the creation of electronic design documents on an unprecedented scale, while determining and finding what can be reused for a new design is like searching for a \u27needle in a haystack\u27. (…) The availability of such extensive knowledge resources is creating new challenges as well as opportunities for research on how to retrieve and reuse the knowl-edge from existing designs.\u27 [1] If the requested knowledge is implicit (which means that it is only in the minds of the employees of a company) the retrieval and reuse of knowledge is even more com-plicated. By representing the (engineering) data backbone of a company, PDM systems are the software implementation which should support the designer to retrieve information about existing and successful design projects. This paper shows that the known data classification approaches of common PDM systems are not applicable to represent implicit (tacit) knowledge. Furthermore a new approach to knowledge representation is introduced by using Semantic Networks. The feasibility of the presented work is shown by a use-case scenario in which the conventional PDM system supported product development process is compared with the proposed way by using the soft-ware \u27The Semaril\u27 — a software tool developed at the Institute of Engineering Design/CAD based on Semantic Networks [2]