|Anibal H. Carmona, President of CESSI (Cámara de la Industria Argentina de Software)|
"An Overview of the Software Industry in Argentina in 2017"
|Miguel Katrib, University of Havana |
"Which Programming Language to choose?"
Short Bio. Miguel Katrib is a Dr by the Lomonosov University of Moscow and by the University of Havana. Senior professor of the Programming and Software Engineering of the Mathematics and Computer Science Department of the University of Havana and Director of the Master in Science Program in Computer Science. Vice-president of the Cuban National doctoral tribunal in Mathematics and Computer Science. Titular member of the Cuban Academy of Sciences and Honored Member of the Scientific Conceal of the University of Havana. Author of several technical papers and 6 books in Programming and Programming Languages. Microsoft invited speaker in some Spanish and Latin-American forums and invited lecturer in several universities in this region. He is a founder of the IDEAS-CibSE workshops. He is also a cinema fan and a good paella chef.
|Roel Wieringa, University of Twente, The Netherlands|
"Design theories in software engineering research"
Abstract. In recent years, the ability to analyze large amounts of data has called into doubt the need for scientific theories. A simple response to this is that predictive theories are scientific theories too. A more substantial response is that, even though correlation is sufficient for prediction, it is not sufficient for the explanation of effects in terms of causes, mechanisms or reasons. Whenever we want to understand the phenomena we study, we should look for causes, mechanisms, or reasons, which means that we should build scientific theories. However, too many research papers in information systems, artificial intelligence and related disciplines fail to state their theoretical contribution. Technical research papers may present new designs without adequate explanation of why they work; empirical papers may make impressive use of statistics without clear contribution to theoretical understanding; or at the other extreme, interpretative research papers may contain convoluted text about philosophical approaches and theoretical contributions, of which the contents may disappear into thin air if analyzed closely. In this keynote I provide a birds eye view on how to produce a clear and defensible theoretical contribution. I focus on design theories and start with a brief introduction to the design and engineering cycles. I will then zoom in on the role of problem theories and design theories in the design cycle. I will then review the structure of design theories, and identify the different kinds of steps that researchers follow to reason from data to theories: descriptive, statistical, abductive and analogic inference. The keynote will be illustrated by examples from software engineering and from other engineering disciplines.
Short Bio. Roel Wieringa occupies the chair of Information Systems at the Department of Computer Science at the University of Twente, The Netherlands. His research interests include requirements engineering, conceptual modelling, and research methodology for information systems, software engineering and the design sciences. He has written three books, Requirements Engineering: Frameworks for Understanding (Wiley, 1996), Design Methods for Reactive Systems: Yourdon, Statemate and the UML (Morgan Kaufmann, 2003, and Design Science Methodology for Information Systems and Software Engineering (Springer, 2014). Find more at http://wwwhome.ewi.utwente.nl/~roelw/.
|Natalia Juristo, Universidad Politécnica de Madrid |
"Use and Misuse of the term experiment in the software repositories research"
Abstract. Today empiricism is everywhere in SE research. But this does not imply that SE is empirically mature. Conducting empirical studies does not mean they are carried out and used properly. In this talk I focus on a methodological issue regarding research on mining software repositories (MSR). MSR is an extremely active area of research these days, but a young one that I believe still lacks rigor. I have observed that the term experiment is misused very often in MSR works. We have conducted a small-scale literature review to understand the level of misuse and it is broad. The results of such review are shown in the talk. I will discuss about the essential features that make an experiment an experiment and allows discovering causality. Most MSR works lack the manipulation required to an empirical study to be an experiment. To me most MSR studies are observational studies. (Although there are some type of experiments that can be conducted with repositories). To get reliable results it is critical that the researchers understand the type of study they are conducting as well as the type of evidence that every type of study generates. I see MSR research as epidemiologic research in medicine. If properly conducted, epidemiologic studies can catch a glimpse of causality. Epidemiology has developed types of empirical studies that make evidence stronger (as control-case studies or cohort studies). MSR could learn from them and apply strategies, as random selection of data from the repository, which makes decrease bias in results.
Short Bio. Natalia Juristo is full professor of software engineering with the Computing School at the Technical University of Madrid (UPM) and is the coordinator of a European Master on SE with the participation of the Universities of Bolzano (Italy), Kaiserslautern (Germany), and Blekinge (Sweden). Natalia has served on Organizing Committees for SEKE97, SEKE01, ESEM07, and for the ICSE03 workshop, "Bridging the gap between HCI and SE". She has also served in the capacities of General Chair (ESEM07, SNPD02 and SEKE01) and Program Chair (ISESE04 and SEKE97). Additionally, Natalia has served on a number of Program Committees (including ICSE, RE, REFSQ, ESEM, ISESE), has been member of several Editorial Boards (including IEEE Software and the Journal of Empirical Software Engineering), and has been Guest Editor of special issues in several journals (including IEEE Software, the Journal of Software and Systems, Data and Knowledge Engineering, and the International Journal of Software Engineering and Knowledge Engineering). Natalia earned her B.S. and Ph.D. degrees in Computing from UPM.