主讲人简介：

Artur Andrzejak has received a PhD degree in computer science from ETH Zurich in 2000 and a habilitation degree from FU Berlin in 2009. He was a postdoctoral researcher at the HP Labs Palo Alto from 2001 to 2002 and a researcher at ZIB Berlin from 2003 to 2009. He was leading the CoreGRID Institute on System Architecture (2004 to 2006) and acted as a Deputy Head of Data Mining Department at I2R Singapore in 2010. Since 2010 he is a professor at Ruprecht-Karls-University of Heidelberg and leads there the Parallel and Distributed Systems group. His research interests include reliability of complex software systems, scalable data analysis, and cloud computing.

报告摘要：

The number of software developers worldwide has increased by roughly 40% in the last years, from 18 million in 2014 to est. 26 million by 2019.  This indicates several challenges: How to support young, potentially inexperienced software engineers in creating high-quality, reliable software? How to reduce the overall cost of software quality assurance in face of increasingly complex and interdependent systems?

In this talk we illustrate how we can address some of these challenges by exploiting data analysis for software testing and debugging. We first discuss two approaches for automated diagnosis of software issues which manifest in a production settings, and thus might have a significant economic impact. First one is a data-driven method for detecting and isolating latent software defects causing the expensive phenomenon of software aging (e.g. memory leaks). The other method targets automated debugging of errors in configuration options of complex applications. Such errors are responsible for a significant share of system downtimes. Finally, we briefly review how scalable machine learning could help in recognizing spurious test results in a very large software project at SAP SE.

As a wrap up, we outline some conclusions and perspectives from the points of view of research.