SMILESENG hosts a number of specialised seminars in the areas of SBSE, ML, empirical methods and their intersections applied to software engineering.

In time sequence, these seminars are:

Dr. Robert Feldt (22 June – 9:30am)

Bayesian Analysis of Software Engineering Data (slides)

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.


Dr. Feldt is a Professor of Software Engineering at Chalmers University of Technology in Gothenburg, where he is part of the Software Engineering 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 focus 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 human aspects; an area we have proposed to call Behavioral Software Engineering.

Dr. Davide Di Ruscio (22 June – 3:00pm)

Intelligent Recommender Systems in Software Development (slides)

Recommender systems are a crucial component of several online shopping systems, allowing business owners to offer personalized products to customers. Recommender systems in software engineering (RSSE) have been conceptualized on a comparable basis, i.e., they assist developers in navigating large information spaces and getting instant recommendations that are helpful to solve a particular development task. In this sense, RSSE provides developers with valuable suggestions, which may consist of different items, such as code examples, topics, third-party components, documentation, to name a few. However, developing RSSE is a complex task; technical choices must be taken to overcome issues related to several aspects, including the lack of baselines, limited data availability, decisions about the performance measures, and evaluation approaches.

This seminar makes an overview of RSSE and describes the challenges that have been encountered in developing different RSSE in the context of the EU CROSSMINER project. Specific attention will be devoted to presenting the intricacies related to the development and evaluation techniques that have been employed to conceive and evaluate the CROSSMINER recommender systems. Moreover, the lessons that have been learned while working on the project will also be discussed.


Davide Di Ruscio is Associate Professor at the Department of Information Engineering Computer Science and Mathematics of the University of L’Aquila. His main research interests are related to several aspects of Software Engineering, Open Source Software, and Model Driven Engineering (MDE) including domain specific modelling languages, model transformation, model differencing, coupled evolution, and recommendation systems. He has published more than 140 papers in various journals, conferences and workshops on such topics. He has been co-guest editor of a number of special issues. He has been in the PC and involved in the organization of several workshops and conferences, and reviewer of many journals like IEEE Transactions on Software Engineering, Science of Computer Programming, Software and Systems Modeling, and Journal of Systems and Software. He is member of the steering committee of the International Conference on Model Transformation (ICMT), of the Software Language Engineering (SLE) conference, of the Seminar Series on Advanced Techniques & Tools for Software Evolution (SATTOSE), of the Workshop on Modelling in Software Engineering at ICSE (MiSE) and of the International Workshop on Robotics Software Engineering (RoSE). Davide is in the editorial board of the International Journal on Software and Systems Modeling (SoSyM), of IEEE Software, of the Journal of Object Technology, and of the IET Software journal.

Dr. Sebastiano Panichella and Mr. Christian Birchler (23 June – 11:00pm)

Testing with Fewer Resources: Toward Adaptive Approaches for Cost-effective Test Generation and Selection (slides)

After a very brief introduction to the basic concepts of SBST (search-based software testing), we will go into adaptive approaches to cost-effecting test generation for Java systems, which will be demonstrated in a short demo. The seminar will continue with the basics of self-driving cars development and testing, and will end with a more detailed discussion on test regression (particularly selection strategies) for self-driving cars software. A demo will also be given on the latter topic, giving the seminar a practical character


Dr. Sebastiano Panichella is a passionate Computer Science Researcher at the Zurich University of Applied Science (ZHAW). He received the PhD in Computer Science from the University of Sannio (Department of Engineering) in 2014 defending the thesis entitled ''Supporting Newcomers in Open Source Software Development Projects''. His main research goal is to conduct industrial research, involving both industrial and academic collaborations, to sustain the Internet of Things (IoT) vision, where future "smart cities" will be characterized by millions of smart systems (e.g., cyber-physical systems such as drones, and other autonomous vehicles) connected over the internet, composed by AI-components, and/or controlled by complex embedded software implemented for the cloud. His research interests are in the domain of Software Engineering (SE), cloud computing (CC), and Data Science (DS): DevOps (e.g., Continuous Delivery, Continuous integration), Machine learning applied to SE, Software maintenance and evolution (with particular focus on Cloud, mobile, AI-based, and Cyber-physical applications), Mobile Computing. He is Review Board member of the EMSE journal.

Christian Birchler is a Research Assistant at the Zurich University of Applied Sciences where he is working on the EU Horizon project COSMOS ("DevOps for Complex Cyber-physical Systems"). He studied at the University of Zurich Software Systems with Applied Probability and Statistics as a minor subject. Currently, he is pursuing a master's degree in Software Systems with Data Science as a minor subject at the University of Zurich. During his studies, he mainly focused on software testing. His research interests are search-based software testing and fuzzing. In his ongoing work, he is investigating the area of software testing and testing in virtual environments combined with the development of tools to solve the problems in his research area. A prominent example is SDC-Scissor (, which is a tool that leverages the test selection part of the regression testing process for self-driving cars software. His vision is to provide a regression testing framework that also includes test prioritization for simulation-based testing of cyber-physical systems.

Dr. Leandro Minku (24 June – 11:30pm)

Data Mining Algorithms Using/Used-by Optimizers: a DUO Approach to Software Engineering

The fields of Software Analytics and Search-Based Software Engineering have evolved mostly as separate fields over the past decades. Both have achieved a great level of maturity, finding their way into Software Engineering Practice. However, their achievements are limited by their isolated focus on either data mining / machine learning or search-based optimisation. What could Software Analytics achieve when using search-based optimisation? And what could Search-Based Software Engineering achieve when using data mining? This talk will discuss recent advancements and future directions of the emerging field of DUO — Data mining Using/Used-by Optimizers for empirical studies in software engineering


Dr. Leandro L. Minku is an Associate Professor at the School of Computer Science, University of Birmingham (UK). Prior to that, he was a Lecturer at the University of Leicester (UK), and a Research Fellow at the University of Birmingham (UK). He received the PhD degree in Computer Science from the University of Birmingham (UK) in 2010. Dr. Minku's main research interests include machine learning for software engineering, machine learning for non-stationary environments / data stream mining, class imbalance learning and search-based software engineering. Among other roles, Dr. Minku is Associate Editor-in-Chief for Neurocomputing, and Associate Editor for IEEE Transactions on Neural Networks and Learning Systems, Empirical Software Engineering journal and Journal of Systems and Software. He was the general chair for the International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE 2019-2020), co-chair for the Artifacts Evaluation Track of the International Conference on Software Engineering (ICSE 2020), and program committee member for prestigious conferences both in the fields of machine learning and software engineering, such as IJCAI, AAAI and ICSE.