The process for requirements elicitation has traditionally been based on textual descriptions or graphical models using UML. While these may have worked for the design of desktop-based systems, we argue, that these notations are not adequate for a dialog with mobile end users, in particular for end users in “blue collar” application domains. We propose an alternative modelling technique “Software Cinema” based on the use of digital videos. We discuss one particular example of using Software cinema in the design of a user interface for a navigation system of a mobile end user.
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In FIPA-style multi-agent systems, agents coordinate their activities by sending messages representing particular communicative acts (or performatives). Agent communication languages must strike a balance between simplicity and expressiveness by defining a limited set of communicative act types that fit the communication needs of a wide set of problems. More complex requirements for particular problems must then be handled by defining domain-specific predicates and actions within ontologies. This paper examines the communication needs of a multi-agent distributed information retrieval system and discusses how well these are met by the FIPA ACL.
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A modularised connectionist model, based on the Mixture of Experts (ME) algorithm for time series prediction, is introduced. A set of connectionist modules learn to be local experts over some commonly appearing states of a time series. The dynamics for mixing the experts is a Markov process, in which the states of a time series are regarded as states of a HMM. Hence, there is a Markov chain along the time series and each state associates to a local expert. The state transition on the Markov chain is the process of activating a different local expert or activating some of them simultaneously by different probabilities generated from the HMM. The state transition property in the HMM is designed to be time-variant and conditional on the first order dynamics of the time series. A modified Baum–Welch algorithm is introduced for the training of the time-variant HMM and it has been proved that by EM process the likelihood function will converge to a local minimum. Experiments, with two time series, show this approach achieves significant improvement in the generalisation performance over global models.
Keywords: series prediction, Mixture of Experts, HMM, connectionist model, expectation and maximization, Gauss probability density distribution
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Accurate effort prediction is often an important factor for successful software development. However, the diversity of software development tools observed today has resulted in a situation where existing effort prediction models’ applicability appears to be limited. Data-centred fourth-generation-language (4GL) software development provides one such difficulty. This paper aims to construct an accurate effort prediction model for data-centred 4GL development where a specific tool suite is used. Using historical data collected from 17 systems developed in the target environment, several linear regression models are constructed and evaluated in terms of two commonly used prediction accuracy measures, namely the mean magnitude of relative error (MMRE) and pred measures. In addition, R2, the maximum value of MRE, and statistics of the absolute residuals are used for comparing the models. The results show that models consisting of specification-based software size metrics, which were derived from Entity Relationship Diagrams (ERDs) and Function Hierarchy Diagrams (FHDs), achieve good prediction accuracy in the target environment. The models’ good effort prediction ability is particularly beneficial because specification-based metrics usually become available at an early stage of development. This paper also investigates the effect of developers’ productivity on effort prediction and has found that inclusion of productivity improves the models’ prediction accuracy further. However, additional studies will be required in order to establish the best productivity inclusive effort prediction model.
Keywords: prediction systems, 4GL, effort, metrics, empirical analysis
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It is a standard aim to complete tasks efficiently and effectively. When technology is involved, the tools must be designed to facilitate optimal performance. The ActualDepth™ Multi-Layer Display (MLD™) is a ‘new generation’ display, consisting of two layered Liquid Crystal Displays (LCDs), with a region of space between them. The top LCD displays transparently, allowing both layers to be viewed simultaneously. This paper describes an experiment that investigated relative reading speeds, error detection, comprehension speeds and comprehension accuracy on the MLD™, including a comparison with standard single layered displays. A framework pertaining to colour and transparency usage on the MLD™ was then developed, which is intended to enhance the usability and effectiveness of the display. In general, it was found that overall readability was improved on the MLD™, compared to a standard display, and different transparency levels and colours should be employed, depending on the purpose of reading the text.
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