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

Modelling the relationship between planning, control, perception and execution behaviours in interactive worksystems

This paper presents a model of planning carried out by interactive worksystems which attempts: 1. To describe the relationship between planning, control, perception and execution behaviours; 2. To make explicit how these may be distributed across the user and physically separate devices. Such a model, it is argued, is more suitable to support HCI design practice than theories of planning in cognitive science which focus on problem-solving methods and representations. To demonstrate the application of the model to work situations, it is illustrated by examples drawn from an observational study of secretarial office administration

Subjective information visualizations

Information Visualizations (InfoViz) are systems that require high levels of cognitive processing. They revolve around the notion of decoding and interpreting visual patterns in order to achieve certain goals. We argue that purely designing for the visual will not allow for optimum experiences since there is more to InfoViz than just the visual. Interaction is a key to achieving higher levels of knowledge. In this position paper we present a different perspective on the underlying meaning of interaction, where we describe it as incorporating both the visual and the physical activities. By physical activities we mean the physical actions upon the physical input device/s. We argue that interaction is the key element for supporting users’ subjective experiences hence these experiences should first be understood. All the discussions in this paper are based upon on going work in the field of visualizing the literature knowledge domain (LKDViz)

Reasoning about order errors in interaction

Reliability of an interactive system depends on users as well as the device implementation. User errors can result in catastrophic system failure. However, work from the field of cognitive science shows that systems can be designed so as to completely eliminate whole classes of user errors. This means that user errors should also fall within the remit of verification methods. In this paper we demonstrate how the HOL theorem prover [7] can be used to detect and prove the absence of the family of errors known as order errors. This is done by taking account of the goals and knowledge of users. We provide an explicit generic user model which embodies theory from the cognitive sciences about the way people are known to act. The user model describes action based on user communication goals. These are goals that a user adopts based on their knowledge of the task they must perform to achieve their goals. We use a simple example of a vending machine to demonstrate the approach. We prove that a user does achieve their goal for a particular design of machine. In doing so we demonstrate that communication goal based errors cannot occur

Verification-guided modelling of salience and cognitive load

Well-designed interfaces use procedural and sensory cues to increase the cognitive salience of appropriate actions. However, empirical studies suggest that cognitive load can influence the strength of those cues. We formalise the relationship between salience and cognitive load revealed by empirical data. We add these rules to our abstract cognitive architecture, based on higher-order logic and developed for the formal verification of usability properties. The interface of a fire engine dispatch task from the empirical studies is then formally modelled and verified. The outcomes of this verification and their comparison with the empirical data provide a way of assessing our salience and load rules. They also guide further iterative refinements of these rules. Furthermore, the juxtaposition of the outcomes of formal analysis and empirical studies suggests new experimental hypotheses, thus providing input to researchers in cognitive science

A software toolkit for web-based virtual environments based on a shared database

We propose a software toolkit for developing complex web-based user interfaces, incorporating such things as multi-user facilities, virtual environments (VEs), and interface agents. The toolkit is based on a novel software architecture that combines ideas from multi-agent platforms and user interface (UI) architectures. It provides a distributed shared database with publish-subscribe facilities. This enables UI components to observe the state and activities of any other components in the system easily. The system runs in a web-based environment. The toolkit is comprised of several programming and other specification languages, providing a complete suite of systems design languages. We illustrate the toolkit by means of a couple of examples