ACQUIRING APPLICATION-SPECIFIC KNOWLEDGE DURING DESIGN TO SUPPORT SYSTEMS MAINTENANCE

Most large systems development efforts proceed in a top-down fashion where initial specifications and requirements are incorporated into a high-level design, followed by programs based on this design. However, a major part of the software life-cycle effort is devoted to maintenance. While several existing methodologies aid in the initial phases of requirements and specification, they have proven to be of little value for maintenance. Changes in user requirements are often translated directly to the level of code, divorcing it from the high level design it was based on. After a few such changes, the programs may not correspond to any formal high-level design, making subsequent maintenance difficult. We argue that maintenance must be based on the knowledge used in synthesizing the high-level design. This requires a development environment where the knowledge about high-level designs is formally represented, and raises the question about how this knowledge will be acquired by the support environment in the first place. In this paper, we present a model that enables the support environment to acquire design knowledge through "learning by observation" of a designer engaged in specifying a high-level design. The knowledge that the learning system begins with is a generic object for expressing design decisions. Based on the input provided by the designer, and a limited interactive querying process, it constructs and continuously refines a taxonomic classification of application-specific knowledge and rules at an appropriate level of generality that capture the rationale of the design. This knowledge can be used subsequently for maintaining system designs and recognizing design situations similar to the ones it has knowledge about.Information Systems Working Papers Serie

Knowledge engineering with semantic web technologies for decision support systems based on psychological models of expertise

Machines that provide decision support have traditionally used either a representation of human expertise or used mathematical algorithms. Each approach has its own limitations. This study helps to combine both types of decision support system for a single system. However, the focus is on how the machines can formalise and manipulate the human representation of expertise rather than on data processing or machine learning algorithms. It will be based on a system that represents human expertise in a psychological format. The particular decision support system for testing the approach is based on a psychological model of classification that is called the Galatean model of classification. The simple classification problems only require one XML structure to represent each class and the objects to be assigned to it. However, when the classification system is implemented as a decision support system within more complex realworld domains, there may be many variations of the class specification for different types of object to be assigned to the class in different circumstances and by different types of user making the classification decision. All these XML structures will be related to each other in formal ways, based on the original class specification, but managing their relationships and evolution becomes very difficult when the specifications for the XML variants are text-based documents. For dealing with these complexities a knowledge representation needs to be in a format that can be easily understood by human users as well as supporting ongoing knowledge engineering, including evolution and consistency of knowledge. The aim is to explore how semantic web technologies can be employed to help the knowledge engineering process for decision support systems based on human expertise, but deployed in complex domains with variable circumstances. The research evaluated OWL as a suitable vehicle for representing psychological expertise. The task was to see how well it can provide a machine formalism for the knowledge without losing its psychological validity or transparency: that is, the ability of end users to understand the knowledge representation intuitively despite its OWL format. The OWL Galatea model is designed in this study to help in automatic knowledge maintenance, reducing the replication of knowledge with variant uncertainties and support in knowledge engineering processes. The OWL-based approaches used in this model also aid in the adaptive knowledge management. An adaptive assessment questionnaire is an example of it, which is dynamically derived using the users age as the seed for creating the alternative questionnaires. The credibility of the OWL Galatea model is tested by applying it on two extremely different assessment domains (i.e. GRiST and ADVANCE). The conclusions are that OWLbased specifications provide the complementary structures for managing complex knowledge based on human expertise without impeding the end users’ understanding of the knowledgebase. The generic classification model is applicable to many domains and the accompanying OWL specification facilitates its implementations

Intelligent alarms detection for the analysis of system fault impact on business

The tools for fault impact analysis are important for the deployment of critical mission systems. These tools can be also used as a development phase aid. We introduce several concepts related to "business alarms". Business alarms are an approximation to the company's business conceptual scheme driven by the business rules from systems conceptual schemes. In order to specify them we propose the utilization of Knowledge Engineering typical techniques. The object of alarm detection for impact analysis of fault business systems is to reduce the breach between business controls and typical control system. For it, furthermore specifying an alarm visualization system for the direction responsible of the systems, we are going to specificity the alarm transmission to a center with following purpose: Development support Help desk with an end user problems and resolution expert database Contingency maneuvers coordination The rush in the projects makes that most of the time the errors are not taken into account into the development phase, and a transactional structure for handling them is added later. Sometimes this structure is not completely implemented and the error handling is left to the database engines and operating systems mechanisms. The fact of the existence of a development support center can facilitate and allow the adding of code chunks with the purpose of centralized debugging. The support center support will have a rule based main kernel that will do a nexus between development and help desk to final users. This kernel will allow: give a greater quality help to final users relate user faults with systems faults in the development manager format. Moreover, the own nature of the objectives, this eases the acceptation of the knowledge engineer in the organization (one of the primary steps to make possible knowledge acquisition). It’s going to be a company’s direction responsibility to present him as the developer of this center and help desk for development and final users. The role of the knowledge engineer covers a wide spectrum in a critical mission environment, from the support of the original design to the startup and tuning of the end user’s help desk. Due to the strong relationship between time and alarms, we verified the importance of having and unique time for all the company’s computer systems, or provide the necessary mechanism in order to adjust the time registry of each separate system. The formalization of rules and knowledge in the area of critical mission systems allows the company to make predictions of the dimensioning and correct forecasting of the systems taking into account their critical nature. As results of the processes of systematization and formalization of knowledge we have: Rules for the platform specification in order to forecast a scheme of high availability and contingency. We documented the need of the creation of a software alarms and a development support center based in this alarms. The cost issues related to the startup of an end user help desk were verified. At last, a prototype program with demonstration purpose was build in order to show the exposed concepts.Sistemas Inteligentes - Sesión de póstersRed de Universidades con Carreras en Informática (RedUNCI

Tools for producing formal specifications : a view of current architectures and future directions

During the last decade, one important contribution towards requirements engineering has been the advent of formal specification languages. They offer a well-defined notation that can improve consistency and avoid ambiguity in specifications. However, the process of obtaining formal specifications that are consistent with the requirements is itself a difficult activity. Hence various researchers are developing systems that aid the transition from informal to formal specifications. The kind of problems tackled and the contributions made by these proposed systems are very diverse. This paper brings these studies together to provide a vision for future architectures that aim to aid the transition from informal to formal specifications. The new architecture, which is based on the strengths of existing studies, tackles a number of key issues in requirements engineering such as identifying ambiguities, incompleteness, and reusability. The paper concludes with a discussion of the research problems that need to be addressed in order to realise the proposed architecture

Common Sense Knowledge, Ontology and Text Mining for Implicit Requirements

The ability of a system to meet its requirements is a strong determinant of success. Thus effective requirements specification is crucial. Explicit Requirements are well-defined needs for a system to execute. IMplicit Requirements (IMRs) are assumed needs that a system is expected to fulfill though not elicited during requirements gathering. Studies have shown that a major factor in the failure of software systems is the presence of unhandled IMRs. Since relevance of IMRs is important for efficient system functionality, there are methods developed to aid the identification and management of IMRs. In this paper, we emphasize that Common Sense Knowledge, in the field of Knowledge Representation in AI, would be useful to automatically identify and manage IMRs. This paper is aimed at identifying the sources of IMRs and also proposing an automated support tool for managing IMRs within an organizational context. Since this is found to be a present gap in practice, our work makes a contribution here. We propose a novel approach for identifying and managing IMRs based on combining three core technologies: common sense knowledge, text mining and ontology. We claim that discovery and handling of unknown and non-elicited requirements would reduce risks and costs in software development

Enabling High-Level Application Development for the Internet of Things

Application development in the Internet of Things (IoT) is challenging because it involves dealing with a wide range of related issues such as lack of separation of concerns, and lack of high-level of abstractions to address both the large scale and heterogeneity. Moreover, stakeholders involved in the application development have to address issues that can be attributed to different life-cycles phases. when developing applications. First, the application logic has to be analyzed and then separated into a set of distributed tasks for an underlying network. Then, the tasks have to be implemented for the specific hardware. Apart from handling these issues, they have to deal with other aspects of life-cycle such as changes in application requirements and deployed devices. Several approaches have been proposed in the closely related fields of wireless sensor network, ubiquitous and pervasive computing, and software engineering in general to address the above challenges. However, existing approaches only cover limited subsets of the above mentioned challenges when applied to the IoT. This paper proposes an integrated approach for addressing the above mentioned challenges. The main contributions of this paper are: (1) a development methodology that separates IoT application development into different concerns and provides a conceptual framework to develop an application, (2) a development framework that implements the development methodology to support actions of stakeholders. The development framework provides a set of modeling languages to specify each development concern and abstracts the scale and heterogeneity related complexity. It integrates code generation, task-mapping, and linking techniques to provide automation. Code generation supports the application development phase by producing a programming framework that allows stakeholders to focus on the application logic, while our mapping and linking techniques together support the deployment phase by producing device-specific code to result in a distributed system collaboratively hosted by individual devices. Our evaluation based on two realistic scenarios shows that the use of our approach improves the productivity of stakeholders involved in the application development

Using multiple criteria decision analysis to aid the selection of enterprise resource planning software : a case study

BHC Ltd is a family owned SME which specialises in steel fabrication for the construction industry. Due to rapid growth over the past decade the company’s current business software has evolved from a collection of semi-integrated individual packages and Excel spreadsheets. To help the company become more efficient during the current financial downturn and to ensure they are capable of future growth, BHC Ltd initiated a project with the University of Strathclyde to select and implement an Enterprise Resource Planning (ERP) solution. This paper will provide a case study of BHC’s ERP selection process. In particular it will discuss how steel specific business requirements and organisational culture led us to use multiple criteria decision analysis (MCDA) when making a final software selection. The MCDA process that was followed is further discussed and includes the success that was achieved by using this approach

Early Identification of Implicit Requirements with the COTIR Approach using Common Sense, Ontology and Text Mining

The ability of a system to meet its requirements is a strong determinant of success. Thus effective Software Requirements Specification (SRS) is crucial. Explicit Requirements are well-defined needs for a system to execute. IMplicit Requirements (IMRs) are assumed needs that a system is expected to fulfill though not elicited during requirements gathering. Studies have shown that a major factor in the failure of software systems is the presence of unhandled IMRs. Since relevance of IMRs is important for efficient system functionality, there are methods developed to aid the identification and management of IMRs. In this research, we emphasize that commonsense knowledge, in the field of Knowledge Representation in AI, would be useful to automatically identify and manage IMRs. This research is aimed at identifying the sources of IMRs and also proposing an automated support tool for managing IMRs within an organizational context. Since this is found to be a present gap in practice, our work makes a contribution here. We propose a novel approach called COTIR (Commonsense, Ontology and Text mining for Implicit Requirements) to identify and manage IMRs. As the name implies, COTIR is based on an integrated framework of three core technologies: commonsense knowledge (CSK), text mining and ontology. We claim that discovery and handling of unknown and non-elicited requirements would reduce risks and costs in software development