Naming the Pain in Requirements Engineering: A Design for a Global Family of Surveys and First Results from Germany

For many years, we have observed industry struggling in defining a high quality requirements engineering (RE) and researchers trying to understand industrial expectations and problems. Although we are investigating the discipline with a plethora of empirical studies, they still do not allow for empirical generalisations. To lay an empirical and externally valid foundation about the state of the practice in RE, we aim at a series of open and reproducible surveys that allow us to steer future research in a problem-driven manner. We designed a globally distributed family of surveys in joint collaborations with different researchers and completed the first run in Germany. The instrument is based on a theory in the form of a set of hypotheses inferred from our experiences and available studies. We test each hypothesis in our theory and identify further candidates to extend the theory by correlation and Grounded Theory analysis. In this article, we report on the design of the family of surveys, its underlying theory, and the full results obtained from Germany with participants from 58 companies. The results reveal, for example, a tendency to improve RE via internally defined qualitative methods rather than relying on normative approaches like CMMI. We also discovered various RE problems that are statistically significant in practice. For instance, we could corroborate communication flaws or moving targets as problems in practice. Our results are not yet fully representative but already give first insights into current practices and problems in RE, and they allow us to draw lessons learnt for future replications. Our results obtained from this first run in Germany make us confident that the survey design and instrument are well-suited to be replicated and, thereby, to create a generalisable empirical basis of RE in practice

Relevance, benefits, and problems of software modelling and model driven techniques—A survey in the Italian industry

Context Claimed benefits of software modelling and model driven techniques are improvements in productivity, portability, maintainability and interoperability. However, little effort has been devoted at collecting evidence to evaluate their actual relevance, benefits and usage complications. Goal The main goals of this paper are: (1) assess the diffusion and relevance of software modelling and MD techniques in the Italian industry, (2) understand the expected and achieved benefits, and (3) identify which problems limit/prevent their diffusion. Method We conducted an exploratory personal opinion survey with a sample of 155 Italian software professionals by means of a Web-based questionnaire on-line from February to April 2011. Results Software modelling and MD techniques are very relevant in the Italian industry. The adoption of simple modelling brings common benefits (better design support, documentation improvement, better maintenance, and higher software quality), while MD techniques make it easier to achieve: improved standardization, higher productivity, and platform independence. We identified problems, some hindering adoption (too much effort required and limited usefulness) others preventing it (lack of competencies and supporting tools). Conclusions The relevance represents an important objective motivation for researchers in this area. The relationship between techniques and attainable benefits represents an instrument for practitioners planning the adoption of such techniques. In addition the findings may provide hints for companies and universitie

The roundtable: an abstract model of conversation dynamics

Is it possible to abstract a formal mechanism originating schisms and governing the size evolution of social conversations? In this work a constructive solution to such problem is proposed: an abstract model of a generic N-party turn-taking conversation. The model develops from simple yet realistic assumptions derived from experimental evidence, abstracts from conversation content and semantics while including topological information, and is driven by stochastic dynamics. We find that a single mechanism - namely the dynamics of conversational party's individual fitness, as related to conversation size - controls the development of the self-organized schisming phenomenon. Potential generalizations of the model - including individual traits and preferences, memory effects and more elaborated conversational topologies - may find important applications also in other fields of research, where dynamically-interacting and networked agents play a fundamental role.Comment: 18 pages, 4 figures, to be published in Journal of Artificial Societies and Social Simulatio

Enterprise Agility: Why Is Transformation so Hard?

Enterprise agility requires capabilities to transform, sense and seize new business opportunities more quickly than competitors. However, acquiring those capabilities, such as continuous delivery and scaling agility to product programmes, portfolios and business models, is challenging in many organisations. This paper introduces definitions of enterprise agility involving business management and cultural lenses for analysing large-scale agile transformation. The case organisation, in the higher education domain, leverages collaborative discovery sprints and an experimental programme to enable a bottom-up approach to transformation. Meanwhile the prevalence of bureaucracy and organisational silos are often contradictory to agile principles and values. The case study results identify transformation challenges based on observations from a five-month research period. Initial findings indicate that increased focus on organisational culture and leveraging of both bottom-up innovation and supportive top-down leadership activities, could enhance the likelihood of a successful transformation

Spatial interactions in agent-based modeling

Agent Based Modeling (ABM) has become a widespread approach to model complex interactions. In this chapter after briefly summarizing some features of ABM the different approaches in modeling spatial interactions are discussed. It is stressed that agents can interact either indirectly through a shared environment and/or directly with each other. In such an approach, higher-order variables such as commodity prices, population dynamics or even institutions, are not exogenously specified but instead are seen as the results of interactions. It is highlighted in the chapter that the understanding of patterns emerging from such spatial interaction between agents is a key problem as much as their description through analytical or simulation means. The chapter reviews different approaches for modeling agents' behavior, taking into account either explicit spatial (lattice based) structures or networks. Some emphasis is placed on recent ABM as applied to the description of the dynamics of the geographical distribution of economic activities, - out of equilibrium. The Eurace@Unibi Model, an agent-based macroeconomic model with spatial structure, is used to illustrate the potential of such an approach for spatial policy analysis.Comment: 26 pages, 5 figures, 105 references; a chapter prepared for the book "Complexity and Geographical Economics - Topics and Tools", P. Commendatore, S.S. Kayam and I. Kubin, Eds. (Springer, in press, 2014

Computational and Robotic Models of Early Language Development: A Review

We review computational and robotics models of early language learning and development. We first explain why and how these models are used to understand better how children learn language. We argue that they provide concrete theories of language learning as a complex dynamic system, complementing traditional methods in psychology and linguistics. We review different modeling formalisms, grounded in techniques from machine learning and artificial intelligence such as Bayesian and neural network approaches. We then discuss their role in understanding several key mechanisms of language development: cross-situational statistical learning, embodiment, situated social interaction, intrinsically motivated learning, and cultural evolution. We conclude by discussing future challenges for research, including modeling of large-scale empirical data about language acquisition in real-world environments. Keywords: Early language learning, Computational and robotic models, machine learning, development, embodiment, social interaction, intrinsic motivation, self-organization, dynamical systems, complexity.Comment: to appear in International Handbook on Language Development, ed. J. Horst and J. von Koss Torkildsen, Routledg

Challenges in Complex Systems Science

FuturICT foundations are social science, complex systems science, and ICT. The main concerns and challenges in the science of complex systems in the context of FuturICT are laid out in this paper with special emphasis on the Complex Systems route to Social Sciences. This include complex systems having: many heterogeneous interacting parts; multiple scales; complicated transition laws; unexpected or unpredicted emergence; sensitive dependence on initial conditions; path-dependent dynamics; networked hierarchical connectivities; interaction of autonomous agents; self-organisation; non-equilibrium dynamics; combinatorial explosion; adaptivity to changing environments; co-evolving subsystems; ill-defined boundaries; and multilevel dynamics. In this context, science is seen as the process of abstracting the dynamics of systems from data. This presents many challenges including: data gathering by large-scale experiment, participatory sensing and social computation, managing huge distributed dynamic and heterogeneous databases; moving from data to dynamical models, going beyond correlations to cause-effect relationships, understanding the relationship between simple and comprehensive models with appropriate choices of variables, ensemble modeling and data assimilation, modeling systems of systems of systems with many levels between micro and macro; and formulating new approaches to prediction, forecasting, and risk, especially in systems that can reflect on and change their behaviour in response to predictions, and systems whose apparently predictable behaviour is disrupted by apparently unpredictable rare or extreme events. These challenges are part of the FuturICT agenda