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Learning and Performance Evolution of Immature Remote Teams in Large-ScaleSoftware Projects: An Industrial Case Study
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.ORCID iD: 0000-0002-7220-9570
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
Ericsson, SWE.
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.ORCID iD: 0000-0003-0639-4234
(English)In: Information and Software Technology, ISSN 0950-5849, E-ISSN 1873-6025Article in journal (Refereed) Submitted
Abstract [en]

Context: Large-scale distributed software projects with long life cycles often involve a considerable amount ofcomplex legacy code. The combination of scale and distribution challenges, and the diculty to acquire knowledgeabout large amounts of complex legacy code may make the onboarding of new developers/teams problematic. Thismay lead to extended periods of low performance.Objective: The main objective of this paper is to analyze the learning processes and performance evolutions (teamproductivity and team autonomy) of remote software development teams added late to a large-scale legacy softwareproduct development, and to propose recommendations to support the learning of remote teams.Method: We conducted a case study in Ericsson, collecting data through archival research, semi-structured interviews,and workshops. We analyzed the collected data using descriptive, inferential and graphical statistics and softqualitative analysis.Results: The results show that the productivity and autonomy of immature remote teams are on average 3.67 and2.27 times lower than the ones of mature teams, respectively. Furthermore, their performance had a steady increaseduring almost the entire first year and dropped (productivity) or got stagnated (autonomy) for a great part of the secondyear. In addition to these results, we also identified four challenges that aected the learning process and performanceevolution of immature remote teams: complexity of the product and technology stack, distance to the main source ofproduct knowledge, lack of team stability, and training expectation misalignment.Conclusion: The results indicate that scale, distribution and complex legacy code may make learning more dicultand demand a long period to achieve high performance. To support the learning of remote teams, we put forward fiverecommendations. We believe that our quantitative analysis, as well as the identified factors and recommendationscan help other companies to onboard new remote teams in large-scale legacy product development projects.

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Software Engineering
Identifiers
URN: urn:nbn:se:bth-15194OAI: oai:DiVA.org:bth-15194DiVA: diva2:1143850
Funder
Knowledge Foundation
Available from: 2017-09-22 Created: 2017-09-22 Last updated: 2017-09-25Bibliographically approved

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