Towards Continuous Data Collection from In-service Products: Exploring the Relation between Data Dimensions and Collection Challenges
Paper i proceeding, 2021

Data collected from in-service products play an important role in enabling software-intensive embedded systems suppliers to embrace data-driven practices. Data can be used in many different ways such as to continuously learn and improve the product, enhance post-deployment services, reduce operational cost or create a better user experience. While there is no shortage of possible use cases leveraging data from in-service products, software-intensive embedded systems companies struggle to continuously collect data from their in-service products. Often, data collection is done in an ad-hoc way and targeting specific use cases or needs. Besides, few studies have investigated data collection challenges in relation to the data dimensions, which are the minimum set of quantifiable data aspects that can define software-intensive embedded product data from a collection point of view. To help address data collection challenges, and to provide companies with guidance on how to improve this process, we conducted a case study at a large multinational telecommunications supplier focusing on data characteristics and collection challenges from the Radio Access Networks (RAN) products. We further investigated the relations of these challenges to the data dimensions to increase our understanding of how data dominions contribute to the challenges.

Software-intensive embedded system

in-service products

Continuous Data Collection


Anas Dakkak

Ericsson AB

Hongyi Zhang

Testing, Requirements, Innovation and Psychology

David Issa Mattos

Chalmers, Data- och informationsteknik, Software Engineering

Jan Bosch

Malmö universitet

Helena Holmström Olsson

Ericsson AB

Proceedings - Asia-Pacific Software Engineering Conference, APSEC

15301362 (ISSN)

Vol. 2021-December 243-252
9781665437844 (ISBN)

28th Asia-Pacific Software Engineering Conference, APSEC 2021
Virtual, Online, Taiwan,


Annan data- och informationsvetenskap




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