Keynote Lectures

Technical Debt - From Financial Metaphor to Daily Practice?
Paris Avgeriou, University of Groningen, Netherlands, Netherlands

High-level Verification and Validation of Software Supporting Business Processes
Hermann Kaindl, TU Wien, Univ. for Continuing Education Krems, Vienna Univ. of Economics and Business, Austria, Austria

Model Me If You Can - Challenges and Benefits of Individual and Mass Data Analysis for Enterprises
Marco Brambilla, Politecnico di Milano, Italy, Italy

Abstract
The term Technical Debt has seen widespread adoption in industry and increased interest in academia over the past years. It expresses quality compromises that can yield short-term benefits but may hurt the long-term evolution of a software system. Technical Debt as a metaphor resonates well with technical and non-technical stakeholders and can potentially act as a communication bridge between them, particularly in making internal software qualities explicit. However, a perfect storm is brewing: Technical Debt is dangerously accumulating in most, if not all, large systems, threatening to “bankrupt” those systems if it is not actively managed. In this talk we revisit the state of the art and practice to examine what theories, tools and methods are offered to help manage Technical Debt. We also identify the challenges that lead to accumulation of technical debt or inability to repay it. Finally, we discuss some promising future directions in the field, concluding with a “call to arms”.

Abstract
High-level Verification and Validation (V&V) of software supporting business processes can be done on the level of a Business Process Model (BPM), since V&V of BPMs indirectly includes an important part of V&V of the software implementing such BPMs (e.g., through service composition). If the BPM is built ‘right’ according to given properties, the software implementing it ‘right’ also satisfies these properties, and if the BPM specifies the ‘right’ process, it is also the ‘right’ software. Semantic specification of services based on formal logic can be used for automated verification of (software) service composition. In order to make such verifications consistent with validations of service compositions in the context of business processes, more and more knowledge needs to be included in the related specifications. Independently of the formalism used, a key challenge is to consistently formalize the process and its properties. While formal verification of business process models (BPMs) can be done through model checking (also known as property checking), formalizing corresponding properties having the process model available may negatively influence the formulation of properties to be checked. In addition, properties should be checkable for several processes. So, we address the problem of formalizing properties without knowing the process model. The deeper issue is that task- and artefact-centric BPMs are mostly used in isolation.
In this context, we developed a new and systematic approach for connecting a task-centric BPM (in BPMN) with a model of an artefact-centric object life cycle through semantic task specification. This allows the formulation of properties for model checking referring to additional models of object life cycles, which together can represent certain business rules. Hence, a combination of conventional business process models (given, e.g., in BPMN), models of business object life cycles, and formalized business rules can be used for verification through model checking.  So, we present a seamless approach for formal and automated verification of BPMs using model checking, and a comprehensive approach to V&V of (software) service design.

Abstract
The current hype on big data, machine learning, and artificial intelligence is starting to affect even the most traditional enterprises and sectors.
In this context, we will explore the potential of large scale data collection and analysis in the area of customer/citizen monitoring, spanning techniques such as crowdsourcing, data fusion, descriptive and predictive analysis, for reconstructing people behaviour and profiling.
The keynote will also consider the perspective of the customer within the context of modern media, where sharing and interactions through digital channels apparently becomes compulsory. Citizens become ready to give up a lot of their information for (marginal?) benefits coming from technology providers that in exchange grab their data.
Through a set of real-world case studies and examples, we will explore techniques, benefits and risks that companies face in approaching user data analysis.