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Invited Talks:


Prof Tim Menzies, CS, NC State

The Future and Promise of Software Engineering Research

[Slides]

Abstract: Software engineering researchers just studying software is like astronomers just studying telescopes. In the 21st century, software is being applied by everybody to everything (see list, below). Yet traditional software engineering research focuses just on the internals of software (e.g. details of programming languages or the particulars of some development process). So this talk is about:

- research for software engineering and
- software engineering for research

Specifically, I ask what software engineering is needed to support the new era of the "citizen scientist". In the 21st century, science has escaped the laboratory and is roaming free in the world. More and more people use software to understand the world around them. What software is needed to better support all that activity?
My comment here will be that we need to be able to mistrust the conclusions of the citizen scientists in the same way we mistrust and evaluate and review and explore and evolve the conclusions of any other scientist. When every citizen can be a scientist (making generalizations from data) it should be possible to for everyone else to

- audit a conclusion;
- to repeat (at least in part) the processes that lead to that conclusion;
- to reconsider parts of old conclusions;
- to decide what a conclusion from some other site does/does not apply locally.

This talk will discuss general software engineering principles for all above, based on recent experiments with the GALE multi-objective optimizer.

Bio: Tim Menzies (Ph.D., UNSW, 1995) is a full Professor in CS at North Carolina State University where he teaches software engineering and automated software engineering. His research relates to synergies between human and artificial intelligence, with particular application to data mining for software engineering. He is the author of over 230 referred publications; and is one of the 100 most cited authors in software engineering out of over 80,000 researchers (http://goo.gl/BnFJs). In his career, he has been a lead researcher on projects for NSF, NIJ, DoD, NASA, USDA, as well as joint research work with private companies. Prof. Menzies is the co-founder of the PROMISE conference series devoted to reproducible experiments in software engineering (http://openscience.us/repo). He is an associate editor of IEEE Transactions on Software Engineering, Empirical Software Engineering, the Automated Software Engineering Journal and the Software Quality Journal. In 2015, he served as co-chair for the ICSE'15 NIER track. In 2016, he serves as co-general chair of ICMSE'16. For more, see his vita (http://goo.gl/8eNhY) or his list of publications (http://scholar.google.com/citations?user=Qmuz0WAAAAAJ) or his home page (http://menzies.us).




Dr Hongyu Zhang, Microsoft Research, Beijing, China

Software Analytics: Data-Driven Software Engineering

Abstract: Over the years of software practice, there is a vast amount of software related data produced by open source projects and by an organization’s local projects. These data include source code, bug reports, emails, change logs, execution traces, metrics, etc. The increase in the volume of software data brings both opportunities and challenges for software engineering researchers and practitioners. Software Analytics is a relatively new research area that focuses on improving software engineering practice by mining insightful and actionable knowledge from data. In this talk, I will briefly introduce some of my recent work on software analytics. I will show how we improve programming and bug management practices using software analytics techniques.

Bio: Hongyu Zhang is currently a lead researcher at Microsoft Research, Beijing, China. Before joining Microsoft in 2014, he was an Associate Professor at Tsinghua University, China. He received the PhD degree from National University of Singapore in 2003. His research is in the area of Software Engineering, in particular, software analytics, quality, maintenance, metrics, and reuse. The main theme of his research is to improve software quality and productivity by mining software data. He has published more than 80 research papers in international journals and conferences, including TSE, TOSEM, ICSE, FSE, ASE, ISSTA, ICSM, ICDM, CIKM, and USENIX ATC. He received two ACM Distinguished Paper awards. He has also served as a program committee member for many software engineering conferences. More information about him can be found at: http://research.microsoft.com/people/honzhang/.