The amounts of customer and product data that are collected by companies across domains is growing exponentially. Connected software-intensive products permeate virtually every aspect of our lives and the actions we take generate data revealing what products are used, when they are used and how they are used. By instrumenting the features they develop, companies can continuously measure what features that add value to customers and how their products perform in the field.
Research, however, shows that companies, despite collecting petabytes upon petabytes of data, make very poor use of this data in their R&D activities, leading to limited effectiveness of R&D in terms of the business value created for the effort that is invested. Some research suggests that half or more of the new features added to typical software systems are waste due to the lack of value created to customers. In addition, research shows that up to 80% or even 90% of R&D resources are spent on commodity functionality that do not add value to customers.
Consequently, there is a significant opportunity for software engineering to increase its effectiveness by adopting evidence-based approaches to software engineering such as experimentation, data analytics and design thinking to accomplish a transformation from the traditional requirements-driven engineering to an outcome-driven engineering approach. However, the transition towards evidence based and experiment driven engineering is far from trivial and companies struggle with extracting value from the data they collect. Even companies with well-established experimentation practices, such as e.g. A/B testing, find it challenging to scale results to involve more than only smaller improvements and optimizations of individual features. Despite access to data, decisions concerning new product development and innovation fall back on opinions and internal assumptions rather than being based upon data and external validation.
In this special session, we invite articles on the following topics (though not limited to):
In particular, we encourage submissions demonstrating the benefits and/or challenges involved when transitioning towards evidence-based and experiment driven engineering, submissions presenting architectures and technologies for experimentation, submissions reporting on experiences when adopting data driven development practices and submissions providing empirical case study data to illustrate how companies approach this shift in development paradigms.
Helena Holmström Olsson Malmo University, Sweden
Jan Bosch Chalmers University of Technology, Sweden
Philipp Leitner, Chalmers University of Technology, Sweden
Christoph Elsner, Siemens AG, Corporate Technology, Germany
Jürgen Münch, Reutlingen University, Germany
Matthias Tischy, Ulm University, Germany
Eric Knauss, Chalmers University of Technology
Klaas-Jan Stol, University of Limerick, Ireland
Daniel Ståhl, Linköping University, Sweden
Casper Lassenius, Aalto University, Finland
Pekka Abrahamsson, University of Jyväskylä, Finland
Slinger Jansen, Utrecht University, Netherlands
Christa Schwanninger, Siemens AG, Corporate Technology, Germany