Recently I had a pleasure to meet at the University College London extraordinary, worldwide researchers working on Machine Learning, Optimization and Software Engineering to explore new approaches to analyse, understand and improve techniques for Software Engineering. Extremely interesting and stimulating discussions influence my research.
Recently, I had the pleasure to visit several companies (mainly focused on software development) and talk about possible areas of research collaboration (e.g. software defect prediction, agile software quality assurance, agile methodologies and practices) with my research team. I attach a short intro about my previous research, as well as selected areas of my research in progress.
We are opened on all difficult problems (especially, but not necessarily, within the software engineering domain) our potential industrial partners are able to pose, including predicting the future.
Btw. There is a famous quote attributed to Niels Bohr: “Prediction is very difficult, especially if it’s about the future.“
My habilitation monograph “Test-Driven Development: An Empirical Evaluation of Agile Practice” is published in both print and electronic formats. Today, Springer updated me on the chapter downloads of my eBook on SpringerLink, Springer’s online platform.
Since its online publication on Dec 22, 2009 until Dec 31, 2011, my eBook has received a total of 1717 chapter download requests. Over the last years the download figures have been as follows: 2011 (978 times), 2010 (639 times), 2009 (100 times).
Marcin Kawalerowicz, the co-author of the book “Continuous Integration in .NET” (http://manning.com/kawalerowicz/), wants to present an overview of the state of the art and recent advances in Continuous Integration and is looking for our advice and ideas to increase his chance of a successful PhD research.
Date: Tuesday May 15, 2012 at 10:00 am
Place: 4.47, B-4, Łukasiewicza 5 street
A friend of mine is looking for two new PhD (post-graduate) students in Software Engineering in Sweden.
As it should be my recommendation, I am ready to recommend only persons who I know personally.
Being a PC Member of the XP’2012 I would like to recommend the conference to all of the students interested in Agile methodologies and software development practices.
As a student volunteer you get: free admission to the conference, the opportunity to listen to talks about agile software development, contact to possible future employers (from both research and industry), conference proceedings, food, a T-shirt.
My students’ startup (PloomWorks) that operates on emerging market of professional mobile solutions for shopping malls and museums is looking for young creative people, who want to learn interesting technologies:
– Android
– iOS
– server-side Spring-based framework etc.
and significantly contribute (internship/employment) in innovative projects (info@ploomworks.com).
I received a free entrance to the Softdevcon IV conference to one of my students. Let me know who is interested in attending the conference.
I’d like to recommend 33rd Degree Conference to my Software Engineering students. See list of speakers and talks.
The movie Moneyball shows the example of how we can look at the data to make better decisions. Baseball is just one area where it can be applied. Software Engineering is another.
I came across the following statements related to “Data Science” topic which (although interdisciplinary in its nature) becomes a buzzword (I believe in Software Engineering too):
“The sexy job in the next ten years will be statisticians… The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill.” (Hal Varian, Google’s Chief Economist interviewed in the McKinsey Quarterly)
“Data Science is a valuable rebranding of computer science and applied statistics skills” (David Smith)
“Data science is clearly a blend of the hackers’ arts…; statistics and machine learning…; and the expertise in mathematics and the domain of the data for the analysis to be interpretable… It requires creative decisions and open-mindedness in a scientific context.” (Hilary Mason and Chris Wiggins in Taxonomy of Data Science)
“…data scientist: those who use both data and science to create something new.” (DJ Patil in Building data science teams)