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.

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.

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.

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.

Goal-oriented modelling is one of the most important research developments in the 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 notation using a set of evidence-based principles for visual notation design. The paper identifies some serious flaws in the i* visual notation together with some recommendations for improvement. A broader goal of the paper is to raise the level of debate and stimulate discussion about visual representation in RE research.

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 complexity of software artifacts is widely believed to be an important determinant of maintenance effort. This paper conducts an experimental analysis of the impact of complexity on the maintenance of the Unified Modeling Language (UML) class diagrams. This represents an analysis of the effect of an internal quality attribute on an external quality attribute. A range of complexity metrics are proposed based on an ontological analysis of the UML language and previous research. The relative influence of these metrics on maintenance effort is then evaluated using a laboratory experiment. A within-subjects design was used, with subjects required to modify a range of UML class diagrams with different levels of complexity. Only two of the metrics emerged as significant determinants of maintenance effort: number of methods and number of associations. Together these explain around 28% of the variation in maintenance effort. While these findings are encouraging, further research is necessary to explore the ability of these metrics to predict maintenance effort. Copyright © 2005 John Wiley & Sons, Ltd.

This paper conducts an empirical analysis of a semiotics-based quality framework for quality assuring information models. 192 participants were trained in the concepts of the quality framework, and used it to evaluate models represented in an extended Entity Relationship (ER) language. A randomised, double-blind design was used, in which each participant independently reviewed multiple models and each model was evaluated by multiple reviewers. A combination of quantitative and qualitative analysis techniques were used to evaluate the results, including reliability analysis, validity analysis, interaction analysis, influence analysis, defect pattern analysis and task accuracy analysis.. An analysis was also conducted of the framework's likelihood of adoption in practice. The study provides strong support for the validity of the framework and suggests that it is likely to be adopted in practice, but raises questions about its reliability and the ability of participants to use it to accurately identify defects. The research findings provide clear directions for improvement of the framework. The research methodology used provides a general approach to empirical validation of quality frameworks.

The traditional thrust of software quality assurance has been to use "brute force" testing at the end of development. However quality assurance in the early phases of development is an order of magnitude more cost effective than testing done at the end of development. This paper describes the introduction of quality reviews as a learning activity into an undergraduate requirements analysis course, and evaluates the effectiveness of this in improving learning. In the absence of any standard instrument for evaluating learning effectiveness, we developed a survey instrument called the Learning Effectiveness Survey. Using this instrument, learning effectiveness is evaluated in the context of the learning goals of the course (short term learning), and in the context of the broader educational programme and future working life (long term learning). It also provides feedback on the learning intervention and how it can be improved. While the intervention was moderately successful in its first application, a great deal was learned about how it could be improved next time.