More for Less: Automated Experimentation in Software-Intensive Systems
Paper i proceeding, 2017

Companies developing autonomous and software-intensive systems show an increasing need to adopt experimentation and data-driven strategies in their development process. With the growing complexity of the systems, companies are increasing their data analytic and experimentation teams to support data-driven development. However, organizations cannot increase in size at the same pace as the system complexity grows. Experimentation teams could run a larger number of experiments by letting the system itself to coordinate its own experiments, instead of the humans. This process is called automated experimentation. However, currently, no tools or frameworks address the challenge of running automated experiments.

This paper discusses, through a set of architectural design decisions, the development of an architecture framework that supports automated continuous experiments. The contribution of this paper is twofold. First, it presents, through a set of architectural design decisions, an architecture framework for automated experimentation. Second, it evaluates the architecture framework experimentally in the context of a human-robot interaction proxemics distance problem. This automated experimentation framework aims to deliver more value from the experiments while using fewer R&D resources.

Continuous experimentation

Architectural design decisions

Automated experimentation

Författare

David Issa Mattos

Software Engineering for People, Architecture, Requirements and Traceability

Jan Bosch

Software Engineering for Testing, Requirements, Innovation and Psychology

Helena Holmström Olsson

Chalmers, Data- och informationsteknik, Software Engineering

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

03029743 (ISSN) 16113349 (eISSN)

Vol. 10611 146-161

The 18th International Conference on Product-Focused Software Process Improvement
Innsbruck, Germany,

Ämneskategorier

Systemvetenskap

Datavetenskap (datalogi)

Datorsystem

DOI

10.1007/978-3-319-69926-4_12

Mer information

Skapat

2019-04-02