Connecting Research and Practice of Software Engineering in the North Netherlands (North SE 2022)

About

As in previous instances, this year’s North SE workshop will bring together software engineering researchers and industry practitioners to explore theoretical and practical aspects, technical challenges and solutions, as well as opportunities for collaboration.

This year, the North SE workshop takes place in the context of a PhD defence, taking advantage of the presence of a number of outstanding software engineering researchers and practitioners from the Netherlands, Belgium and Spain.

Date & Venue

Join us in Groningen, December 19th 2022, Van Swinderen Huys - Club Lounge.

Here is the address of Van Swinderen Huys, and a map on reach the venue and other useful information.

Registration

To register, simply send an email to p.avgeriou@rug.nl (Paris Avgeriou) saying you are attending.

Detailed Program

Abstracts

Nobody expects the Spanish Inquisition! Python culture, idioms and some other crazy (or not) ideas
   by Gregorio Robles

In recent times in my research group we have been investigating issues related to the Python programming language and its dynamic community. In particular we have focused on two issues. On the one hand, we have studied the concept of Pythonic, which is said tobe the most elegant (that is, readable and beautiful) way of programming in Python. In this way, we have explored how Python developers understand the term ‘Pythonic’, built a catalog of ‘Pythonic idioms’ collected from the literature, and made some guesses about the effects of having a specific Python term associated with how it has to be the fancy code. On the other hand, we have been working on identifying a programmer’s level of understanding of Python, inspired from the CEFR framework that is commonly used with natural language (the one that says that one is B1, intermediate level, in Dutch or C2, advanced level, in English). We’d like to be able to determine what level of Python code a developer can understand (and modify), and we’ll talk about what possibilities having such a tool offers us, both for newbies and professionals.

Biography: Gregorio Robles is a Professor at the Universidad Rey Juan Carlos, Madrid, Spain. His main research interest lies around Open Source Software, mining software repositories, and lately on the development of computational thinking in children (e.g., the Dr. Scratch tool). His research group created 10+ years ago a spin-off called Bitergia that does software analytics.

Test Amplification in Python — An Industrial Experience Report
   by Serge Demeyer

Software test amplification is the act of strengthening manually written test-cases to exercise the boundary conditions of the system under test. Several academic tool prototypes have been proposed by the research community so far — DSpot (for Java), AmPyfier (for Python) and Small-Amp (for Pharo-Smalltalk). Up until now, these tool prototypes have only been validated on a series of open source systems; concrete experience reports from actual use within the software industry are still lacking. In this presentation, we will share our experience with AmPyfier as applied within the context of Garvis, a start up company from the University of Antwerp.

Biography: Serge Demeyer is a Professor at the University of Antwerp (Department of Mathematics and Computer Science) and the spokesperson for the NEXOR research consortium. His main research interest concerns software evolution, more specifically how to strike the right balance between reliability (striving for perfection) and agility (optimising for adaptability). He is involved in a few start-ups consulting his expertise on software architecture and test automation.

Sustainable MLOps: Trends and Challenges from Practice
   by Damian Tamburri

Even simply through a GoogleTrends search it becomes clear that Machine-Learning Operations-or MLOps, for short-are climbing in interest from both a scientific and practical perspective. On the one hand, software components and middleware are proliferating to support all manners of MLOps, from AutoML (i.e., software which enables developers with limited machine-learning expertise to train high-quality models specific to their domain or data) to feature-specific ML engineering, e.g., Explainability and Interpretability. On the other hand, the more these platforms penetrate the day-to-day activities of software operations, the more the risk for AI Software becoming unsustainable from a social, technical, or organisational perspective. This talk offers a concise definition of MLOps and AI Software Sustainability and outlines key challenges in its pursuit.

Biography: Damian is an Associate Professor at the Eindhoven University of Technology, the Jheronimus Academy of Data Science, in s’Hertogenbosch, The Netherlands with a double-affiliation at Politecnico di Milano as well. At JADS he lectures the Big Data Engineering and Deep Learning courses, while lending a hand in the Machine-Learning pre-master course. His research interests rotate around Data-Intensive Services DevOps/DataOps, Social Software Engineering, and Artificial-Intelligence Software Engineering. Damian has published over 150+ papers in either top Journals or conferences in Software Engineering, Information Systems, as well as Services and AI Computing. Also, Damian has been an active contributor and lead research in many EU FP6, FP7, H2020, and HorizonEurope projects, such as S-Cube, MODAClouds, SeaClouds, DICE, ANITA, DossierCLOUD, ProTECT, RADON, SODALITE, DESTINI, and more. In addition, Damian is ACM TOSEM editorial board member, secretary of the OASIS TOSCA Standardisation TC as well as secretary of the IFIP TC2, TC6, and TC8 WG on “Service-Oriented Computing”.