Daniel L. Moody’s research while affiliated with Simplot Australia Pty. Ltd. and other places

The success of requirements engineering depends critically on effective communication between business analysts and end users, yet empirical studies show that business stakeholders understand RE notations very poorly. This paper proposes a novel approach to designing RE visual notations that actively involves naïve users in the process. We use i*, one of the most influential RE notations, to demonstrate the approach, but the same approach could be applied to any RE notation. We present the results of 5 related empirical studies that show that novices outperform experts in designing symbols that are comprehensible to novices: the differences are both statistically significant and practically meaningful. Symbols designed by novices increased semantic transparency (their ability to be spontaneously interpreted by other novices) by almost 300% compared to the existing i* notation. The results challenge the conventional wisdom about visual notation design: that it should be conducted by a small group of experts; our research suggests that it should instead be conducted by large numbers of novices. The approach is consistent with Web 2.0, in that it harnesses the collective intelligence of end users and actively involves them in the notation design process as “prosumers” rather than passive consumers. We believe this approach has the potential to radically change the way visual notations are designed in the future.

[Context and motivation] i* is one of the most popular modelling languages in Requirements Engineering. i* models are meant to support communication between technical and non-technical stakeholders about the goals of the future system. Recent research has established that the effectiveness of model-mediated communication heavily depends on the visual syntax of the modelling language. A number of flaws in the visual syntax of i* have been uncovered and possible improvements have been suggested. [Question/problem] Producing effective visual notations is a complex task that requires taking into account various interacting quality criteria. In this paper, we focus on one of those criteria: Semantic Transparency, that is, the ability of notation symbols to suggest their meaning. [Principal ideas/results] Complementarily to previous research, we take an empirical approach. We give a preview of a series of experiments designed to identify a new symbol set for i* and to evaluate its semantic transparency. [Contribution] The reported work is an important milestone on the path towards cognitively effective requirements modelling notations. Although it does not solve all the problems in the i* notation, it illustrates the usefulness of an empirical approach to visual syntax definition. This approach can later be transposed to other quality criteria and other notations.

How classes of things and properties in general should be represented in conceptual models is a fundamental issue. For example, proponents of object-role modelling argue that no distinction should be made between the two constructs, whereas proponents of entity-relationship modelling argue the distinction is important but provide ambiguous guidelines about how the distinction should be made. In this paper, the authors use ontological theory and cognition theory to provide guidelines about how classification should be represented in conceptual models. The authors experimented to test whether clearly distinguishing between classes of things and properties in general enabled users of conceptual models to better understand a domain. They describe a cognitive processing study that examined whether clearly distinguishing between classes of things and properties in general impacts the cognitive behaviours of the users. The results support the use of ontologically sound representations of classes of things and properties in conceptual modelling.

Visual notations form an integral part of the language of software engineering (SE). Yet historically, SE researchers and notation designers have ignored or undervalued issues of visual representation. In evaluating and comparing notations, details of visual syntax are rarely discussed. In designing notations, the majority of effort is spent on semantics, with graphical conventions often an afterthought. Typically no design rationale, scientific or otherwise, is provided for visual representation choices. While SE has developed mature methods for evaluating and designing semantics, it lacks equivalent methods for visual syntax. This tutorial defines a set of principles for designing cognitively effective visual notations: ones that are optimised for human communication and problem solving. Together these form a design theory, called the Physics of Notations as it focuses on the physical (perceptual) properties of notations rather than their logical (semantic) properties. The principles were synthesised from theory and empirical evidence from a wide range of fields and rest on an explicit theory of how visual notations communicate. They can be used to evaluate, compare and improve existing visual notations as well as to construct new ones. The tutorial identifies serious design flaws in some of the leading SE notations together with practical suggestions for improving them. It also showcases some examples of visual notation design excellence from SE and other fields.

Goal-oriented modelling is one of the most important research developments in the requirements engineering (RE) field. This paper conducts a systematic analysis of the visual syntax of i*, one of the leading goal-oriented languages. Like most RE notations, i* is highly visual. Yet surprisingly, there has been little debate about or modification to its graphical conventions since it was proposed more than a decade ago. We evaluate the i* visual notation using a set of principles for designing cognitively effective visual notations (the Physics of Notations). The analysis reveals some serious flaws in the notation together with some practical recommendations for improvement. The results can be used to improve its effectiveness in practice, particularly for communicating with end users. A broader goal of the paper is to raise awareness about the importance of visual representation in RE research, which has historically received little attention. KeywordsGoal modelling- i*-Visualisation-Visual syntax-Evaluation-Visual notation-Visual language

How classes of things and properties in general should be represented in conceptual models is a fundamental issue. For example, proponents of object-role modelling argue that no distinction should be made between the two constructs, whereas proponents of entity-relationship modelling argue the distinction is important but provide ambiguous guidelines about how the distinction should be made. In this paper, the authors use ontological theory and cognition theory to provide guidelines about how classification should be represented in conceptual models. The authors experimented to test whether clearly distinguishing between classes of things and properties in general enabled users of conceptual models to better understand a domain. They describe a cognitive processing study that examined whether clearly distinguishing between classes of things and properties in general impacts the cognitive behaviours of the users. The results support the use of ontologically sound representations of classes of things and properties in conceptual modelling.

Visual notations form an integral part of the language of software engineering (SE). Yet historically, SE researchers and notation designers have ignored or undervalued issues of visual representation. In evaluating and comparing notations, details of visual syntax are rarely discussed. In designing notations, the majority of effort is spent on semantics, with graphical conventions largely an afterthought. Typically, no design rationale, scientific or otherwise, is provided for visual representation choices. While SE has developed mature methods for evaluating and designing semantics, it lacks equivalent methods for visual syntax. This paper defines a set of principles for designing cognitively effective visual notations: ones that are optimized for human communication and problem solving. Together these form a design theory, called the Physics of Notations as it focuses on the physical (perceptual) properties of notations rather than their logical (semantic) properties. The principles were synthesized from theory and empirical evidence from a wide range of fields and rest on an explicit theory of how visual notations communicate. They can be used to evaluate, compare, and improve existing visual notations as well as to construct new ones. The paper identifies serious design flaws in some of the leading SE notations, together with practical suggestions for improving them. It also showcases some examples of visual notation design excellence from SE and other fields.

The goal of this paper is identify the theoretical foundations of the IS field. Currently there is a lack of consensus about what the core IS theories are, or even if we have any at all. If we do, they certainly don’t appear in IS curricula or textbooks as they do in more mature disciplines. So far, most of the debate on this issue has been conducted at a subjective and prescriptive (normative) level. We attempt to broaden the debate by taking a descriptive (positive) approach, using relatively objective data. We do this by consulting the “geological record”: the pattern of citations in the leading IS journals. We use a combination of quantitative and qualitative techniques to identify the most influential theories in the IS field. The results of our analysis are surprisingly positive, especially in the light of warnings about IS being overly dependent on reference disciplines (a discipline with no theory to call its own) and being obsessed with research methodology (emphasising how to research at the expense of what to research). This suggests that the negative views often expressed about the progress of IS may be unjustified and that its development has followed the normal evolutionary pattern of any research field. Being aware of our theoretical foundations will help clarify our disciplinary identity and guide teaching and scholarship.

The goal of this paper is identify the theoretical foundations - the core theories - of the IS field. Currently there is a lack of consensus about what the core IS theories are, or even if we have any at all. If we do, they certainly don't appear in IS curricula or textbooks as they do in more mature disciplines. So far, most of the debate on this issue has been conducted at a subjective and prescriptive (normative) level. We attempt to broaden the debate by taking a descriptive (positive) approach, using relatively objective data. We do this by consulting the "geological record": the pattern of citations in the leading IS journals. We use a combination of quantitative and qualitative techniques to identify the most influential theories in the IS field. The results of our analysis are surprisingly positive, especially in the light of warnings about IS being overly dependent on reference disciplines (a discipline with no theory to call its own) and being obsessed with research methodology (emphasising how to research at the expense of what to research). This suggests that the negative views often expressed about the progress of IS may be unjustified and that its development has followed the normal evolutionary pattern of any research field. Being aware of our theoretical foundations will help clarify our disciplinary identity and guide teaching and scholarship.