Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Experiences from Measuring Learning and Performance in Large-Scale Distributed Software Development
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering. Blekinge Institute of Technology. (SERL-Sweden)ORCID iD: 0000-0002-7220-9570
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering. (SERL-Sweden)
Ericsson, SWE.
2016 (English)In: Proceedings of the 10th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, ACM Digital Library, 2016, 17Conference paper, Published paper (Refereed)
Abstract [en]

Background: Developers and development teams in large-scale software development are often required to learn continuously. Organizations also face the need to train and support new developers and teams on-boarded in ongoing projects. Although learning is associated with performance improvements, experience shows that training and learning does not always result in a better performance or significant improvements might take too long.

Aims: In this paper, we report our experiences from establishing an approach to measure learning results and associated performance impact for developers and teams in Ericsson.

Method: Experiences reported herein are a part of an exploratory case study of an on-going large-scale distributed project in Ericsson. The data collected for our measurements included archival data and expert knowledge acquired through both unstructured and semi-structured interviews. While performing the measurements, we faced a number of challenges, documented in the form of lessons learned.

Results: We aggregated our experience in eight lessons learned related to collection, preparation and analysis of data for further measurement of learning potential and performance in large-scale distributed software development.

Conclusions: Measuring learning and performance is a challenging task. Major problems were related to data inconsistencies caused by, among other factors, distributed nature of the project. We believe that the documented experiences shared herein can help other researchers and practitioners to perform similar measurements and overcome the challenges of large-scale distributed software projects, as well as proactively address these challenges when establishing project measurement programs.

Place, publisher, year, edition, pages
ACM Digital Library, 2016. 17
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:bth-15187DOI: 10.1145/2961111.2962636ISBN: 978-1-4503-4427-2 (electronic)OAI: oai:DiVA.org:bth-15187DiVA: diva2:1143810
Conference
International Symposium on Empirical Software Engineering and Measurement, Ciudad Real, Spain
Funder
Knowledge Foundation
Available from: 2017-09-22 Created: 2017-09-22 Last updated: 2017-09-25Bibliographically approved

Open Access in DiVA

fulltext(435 kB)97 downloads
File information
File name FULLTEXT01.pdfFile size 435 kBChecksum SHA-512
98acfa6a176410b13720d5faa73fa3705f53d8c444d0cf6315b8055479404b6e8e18cfafc6f11c291cccf2da1543638281d67c610341d9acec763f2dac0beff9
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Search in DiVA

By author/editor
Britto, RicardoŠmite, Darja
By organisation
Department of Software Engineering
Software Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 97 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 79 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf