Workshop on Software Metrics and Measurements as Foundations of Big Data, Software Analytics and Machine Learning


Artificial intelligence and machine learning are increasingly often used as part of modern software systems, providing capabilities to learn from and reason about vast quantities of data. The crucial challenge in developing high quality AI/ML-based systems is the ability to use high quality data as inputs. In this workshop, we raise the issue of how software metrics and measurements can support the development and evolution of modern software systems based on AI/ML and how the AI/ML algorithms can be used in better software measurements. 

Call for submissions 

Software measurement is one of the key practices in software engineering, which allows us to develop software in a structured and organized way – i.e. engineer software products. Software measurement generates large quantities of data in modern software development, and this data is often used to create products in a smarter way, e.g. using machine learning (AI/ML 4 SE – AI/ML for Software Engineering). In addition to using machine learning in software development, many software products use machine learning-based components and process large quantities of data to deliver user value, e.g. software predicting disturbances in electrical power grids or software managing telecom networks (SE4AI/ML).  

We solicit the submissions from either or both of the following perspectives – one is how to use AI/ML to improve measurement processes and the other one is how to improve measurement processes so that we get more usable data for the use of machine learning. 


We solicit the submissions in the following, and related, areas: 

  • Using AI and machine learning as part of measurement processes 
  • Autonomous measurements and their quality 
  • Measuring data quality of the training and validation data for AI and machine learning-based systems 
  • Measurement and Monitoring the quality of Big Data systems  
  • Software analytics of AI/ML-based software systems 
  • Software analytics of Big Data and Big Data systems 
  • Data quality and AI/ML in safety critical systems 
  • Software engineering methods for AI/ML systems (SE4AI/ML) 
  • Usage of AI/ML in software development (AI/ML4SE) 


We solicit the following types of contributions: position papers (4-6 pages), research papers (6-10 pages), industry experience reports (4-8 pages). 


Program committee, tentative

  • Alain Abran, École de Technologie Supérieure | UQAM,Canada 
  • Pierre Almèn, Improveit, Sweden 
  • Sousuke Amasaki, Okayama Prefectural University, Japan 
  • Lefteris Angelis, Aristotle University Of Thessaloniki, Greece 
  • Luigi Buglione, École de Technologie Supérieure | Engineering.IT, Italy 
  • Marcus Ciolkowski, Qaware Gmbh, Germany 
  • Beata Czarnacka-Chrobot, Warsaw School Of Economics, Poland 
  • Maya Daneva, University of Twente, The Netherlands 
  • Onur Demirors, Middle East Technical University, Turkey 
  • Luigi Lavazza, Università Degli Studi dell’Insubria, Italy 
  • Rudolf Ramler, Software Competence Center Hagenberg Gmbh, Austria 
  • Martin Shepperd, Brunel University, United Kingdom 
  • Charles Symons, COSMIC, United Kingdom 
  • Ayca Tarhan, Hacettepe University, Turkey 
  • Sylvie Trudel, Université Du Québec, Montréal, Canada 
  • Dietmar Winkler, Vienna University Of Technology, Austria