Case study: Class diagram restructuring

This case study is an update-in-place refactoring transformation on UML class diagrams. Its aim is to remove clones of attributes from a class diagram, and to identify new classes which abstract groups of classes that share common data features. It is used as one of a general collection of transformations (such as the removal of redundant inheritance, or multiple inheritance) which aim to improve the quality of a specification or design level class diagram. The transformation is a typical example of a model refactoring, and illustrates the issues involved in such transformations.Comment: In Proceedings TTC 2013, arXiv:1311.753

The influence of conceptual user models on the creation and interpretation of diagrams representing reactive systems

In system design, many diagrams of many different types are used. Diagrams communicate design aspects between members of the development team, and between these experts and the non-expert customers and future users. Mastering the creation of diagrams is often a challenging task, judging by particular errors persistently found in diagrams created by undergraduate computer science students. We assume a possible misalignment between human perception and cognition on the one hand and the diagrams’ structure and syntax on the other. This article presents the results of an investigation of such a misalignment. We focus on the deployment of so-called 'conceptual user models' (mental models, created by users in their mind) at the creation of diagrams. We propose a taxonomy for mental mappings, used for categorization of representations. We describe an experiment where naive and novice subjects created one or several diagrams of a familiar task. We use our taxonomy for analysing these diagrams, both for the represented task structure and the symbols used. The results indeed show a mismatch between mental models and currently used diagram techniques

Language design for a personal learning environment design language

Approaching technology-enhanced learning from the perspective of a learner, we foster the idea of learning environment design, learner interactions, and tool interoperability. In this paper, we shortly summarize the motivation for our personal learning environment approach and describe the development of a domain-specific language for this purpose as well as its realization in practice. Consequently, we examine our learning environment design language according to its lexis and syntax, the semantics behind it, and pragmatical aspects within a first prototypic implementation. Finally, we discuss strengths, problematic aspects, and open issues of our approach

On empirical methodology, constraints, and hierarchy in artificial grammar learning

This paper considers the AGL literature from a psycholinguistic perspective. It first presents a taxonomy of the experimental familiarization test procedures used, which is followed by a consideration of shortcomings and potential improvements of the empirical methodology. It then turns to reconsidering the issue of grammar learning from the point of view of acquiring constraints, instead of the traditional AGL approach in terms of acquiring sets of rewrite rules. This is, in particular, a natural way of handling long‐distance dependences. The final section addresses an underdeveloped issue in the AGL literature, namely how to detect latent hierarchical structure in AGL response patterns

Approximations in Learning & Program Analysis

In this work we compare and contrast the approximations made in the problems of Data Compression, Program Analysis and Supervised Machine Learning. G\uf6del\u2019s Incompleteness Theorem mandates that any formal system rich enough to include integers will have unprovable truths. Thus non computable problems abound, including, but not limited to, Program Analysis, Data Compression and Machine Learning. Indeed, it can be shown that there are more non-computable functions than computable. Due to non- computability, precise solutions for these problems are not feasible, and only approximate solutions may be computed. Presently, each of these problems of Data Compression, Machine Learning and Program Analysis is studied independently. Each problem has it\u2019s own multitude of abstractions, algorithms and notions of tradeoffs among the various parameters. It would be interesting to have a unified framework, across disciplines, that makes explicit the abstraction specifications and ensuing tradeoffs. Such a framework would promote inter-disciplinary research and develop a unified body of knowledge to tackle non-computable problems. As a small step to that larger goal, we propose an Information Oriented Model of Computation that allows comparing the approximations used in Data Compression, Program Analysis and Machine Learning. To the best of our knowledge, this is the first work to propose a method for systematic comparison of approximations across disciplines. The model describes computation as set reconstruction. Non-computability is then presented as inability to perfectly reconstruct sets. In an effort to compare and contrast the approximations, select algorithms for Data Compression, Machine Learning and Program Analysis are analyzed using our model. We were able to relate the problems of Data Compression, Machine Learning and Program Analysis as specific instances of the general problem of approximate set reconstruction. We demonstrate the use of abstract interpreters in compression schemes. We then compare and contrast the approximations in Program Analysis and Supervised Machine Learning. We demonstrate the use of ordered structures, fixpoint equations and least fixpoint approximation computations, all characteristic of Abstract Interpretation (Program Analysis) in Machine Learning algorithms. We also present the idea that widening, like regression, is an inductive learner. Regression generalizes known states to a hypothesis. Widening generalizes abstract states on a iteration chain to a fixpoint. While Regression usually aims to minimize the total error (sum of false positives and false negatives), Widening aims for soundness and hence errs on the side of false positives to have zero false negatives. We use this duality to derive a generic widening operator from regression on the set of abstract states. The results of the dissertation are the first steps towards a unified approach to approximate computation. Consequently, our preliminary results lead to a lot more interesting questions, some of which we have tried to discuss in the concluding chapter

Methodological development

Book description: Human-Computer Interaction draws on the fields of computer science, psychology, cognitive science, and organisational and social sciences in order to understand how people use and experience interactive technology. Until now, researchers have been forced to return to the individual subjects to learn about research methods and how to adapt them to the particular challenges of HCI. This is the first book to provide a single resource through which a range of commonly used research methods in HCI are introduced. Chapters are authored by internationally leading HCI researchers who use examples from their own work to illustrate how the methods apply in an HCI context. Each chapter also contains key references to help researchers find out more about each method as it has been used in HCI. Topics covered include experimental design, use of eyetracking, qualitative research methods, cognitive modelling, how to develop new methodologies and writing up your research