O professor Sven Apel, da Universidade de Passau (Alemanha), visitará o Centro de Informática (CIn) da UFPE no dia 26 de Setembro. Ele vem colaborar com o SPG (Software Productivity Group), liderado pelo professor Paulo Borba. Nesta ocasião, ele apresentará seu trabalho, Techniques and Prediction Models for Sustainable Product-Line Engineering, às 11h no anfiteatro do centro. A palestra será proferida em inglês.
Abaixo, resumo em inglês do currículo do professor:
Sven Apel is full professor, Chair of Software Product Lines, at University of Passau, Germany;  and member of IFIP Working Group 2.11 (Program Generation), Association for Computing Machinery (ACM) and German Informatics Society (Gesellschaft für Informatik). His research focuses on methods, tools, and theories for the construction of manageable, reliable, adaptable, and evolvable software systems, ranging from embedded systems to large-scale systems. In particular, he is interested in: Programming paradigms (features, aspects, patterns, services); Software engineering (product lines, modeling, architecture, generation); Formal methods (program algebra, program calculi, type systems, verification)  and Systems (embedded, distributed, ubiquitous, organic). In his work, he combines formal and empirical methods. Furthermore, he develops tools to demonstrate the practicality of his research results.
Breve resumo do trabalho a ser apresentado, também em inglês:
Software product-line engineering has gained considerable momentum in recent years, both in industry and in academia. Companies and institutions such as NASA, Hewlett Packard, General Motors, Boeing, Nokia, and Philips apply product-line technology with great success to sustain their development by broadening their product portfolio, improving software quality, shorting time to market, and being able to react faster to market changes. However, pursuing a product-line approach implies often an up-front investment for future benefits. Product-line developers have to anticipate which features will be desired by customers in the future. So, prediction models play an important role to avoid uneconomic developments. However, contemporary prediction models largely ignore structural and behavioral properties of the architecture and implementation assets of a product line. For example, modifying the transaction management of a database system is by far more expensive and risky than modifying its command-line interface. We propose to rethink contemporary prediction models and to employ state-of-the-art analysis techniques to create a richer knowledge base for predictions based on implementation knowledge, including software metrics, static analysis, mining techniques, measurements of non-functional properties, and feature-interaction analysis.


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