Page 15 - SMILESENG

Intl. Summer School on Search- and Machine Learning-based Software Engineering
Bayesian Analysis of Software Engineering Data
Robert Feldt
Chalmers University of Technology
Abstract. Many other scientific fields that rely heavily on analyzing empirical data, e.g. medicine, psychology, and economics, are in a sort of replication crisis. One underlying reason is inadequate statistical practices. There is reason to believe software engineering might not be much better off. In this seminar I will briefly summarize key principles for Bayesian data analysis as well as show examples of how one can apply it for analysis of software engineering data. I will also argue what the benefits are and how it can be one step towards both making our results more robust and help create chains of evidence between multiple studies.
Biography. Dr. Robert Feldt is a Professor of Software Engineering at Chalmers University of Technology in Gothenburg, where he is part of the Software Engineer- ing division at the Department of Computer Science and Engineering. He is also a part-time Professor of Software Engineering at Blekinge Institute of Technology in Karlskrona, Sweden. He is co-Editor in Chief of Empirical Software Engineering (EMSE) Journal. He is interested in Software Engineering but with a particular fo- cus on software testing, requirements engineering, psychological and social aspects as well as agile development methods/practices. He is “one of the pioneers” in the search-based software engineering field (according to an ACM Computing Survey of SBSE) and has a general interest in applying Artificial Intelligence and Machine Learning both during software development and, in general, within software systems. Based on his studies in Psychology he also tries to get more research focused on hu- man aspects; an area we have proposed to call Behavioral Software Engineering.
3