research-article

An empirical study of WIP in kanban teams

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Dag I. K. SjøbergAuthors Info & Claims

ESEM '18: Proceedings of the 12th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement

Article No.: 13, Pages 1 - 8

https://doi.org/10.1145/3239235.3239238

Published: 11 October 2018 Publication History

Abstract

Background: Limiting the amount of Work-In-Progress (WIP) is considered a fundamental principle in Kanban software development. However, no published studies from real cases exist that indicate what an optimal WIP limit should be. Aims: The primary aim is to study the effect of WIP on the performance of a Kanban team. The secondary aim is to illustrate methodological challenges when attempting to identify an optimal or appropriate WIP limit. Method: A quantitative case study was conducted in a software company that provided information about more than 8,000 work items developed over four years by five teams. Relationships between WIP, lead time and productivity were analyzed. Results: WIP correlates with lead time; that is, lower WIP indicates shorter lead times, which is consistent with claims in the literature. However, WIP also correlates with productivity, which is inconsistent with the claim in the literature that a low WIP (still above a certain threshold) will improve productivity. The collected data set did not include sufficient information to measure aspects of quality. There are several threats to the way productivity was measured. Conclusions: Indicating an optimal WIP limit is difficult in the studied company because a changing WIP gives contrasting results on different team performance variables. Because the effect of WIP has not been quantitatively examined before, this study clearly needs to be replicated in other contexts. In addition, studies that include other team performance variables, such as various aspects of quality, are requested. The methodological challenges illustrated in this paper need to be addressed.

References

[1]

H. Kniberg and M. Skarin. 2010. Kanban and Scrum---making the most of both. Lulu.com.

Digital Library

[2]

D. Anderson, G. Concas, M.I. Lunesu, and M. Marchesi. 2011. Studying Lean-Kanban approach using software process simulation. In International Conference on Agile Software Development. Springer, Berlin, Heidelberg, 12--26.

[3]

J.D. Little and S.C. Graves. 2008. Little's law. In Building intuition: insights from basic operations management models and principles (Vol. 115), D. Chhajed and T.J. Lowe (Eds.). Springer Science & Business Media. Springer US, 81--100.

[4]

D.J. Anderson. 2010. Kanban: successful evolutionary change for your technology business. Blue Hole Press.

[5]

D.J. Anderson, G. Concas, M.I. Lunesu, M. Marchesi, M., and H. Zhang. 2012. A comparative study of Scrum and Kanban approaches on a real case study using simulation. In International Conference on Agile Software Development. Springer, Berlin, Heidelberg, 123--137.

[6]

G. Concas, M.I. Lunesu, M. Marchesi, and H. Zhang (2013). Simulation of software maintenance process, with and without a work-in-process limit. Journal of Software: Evolution and Process, 25(12), 1225--1248.

[7]

Al-Baik and J. Miller (2015). The Kanban approach, between agility and leanness: a systematic review. Empirical Software Engineering, 20(6), 1861--1897.

Digital Library

[8]

M.O. Ahmad, J. Markkula, and M. Oivo. 2013. Kanban in software development: A systematic literature review. In 39th Euromicro Conference on Software Engineering and Advanced Applications (SEAA2013). IEEE, 9--16

Digital Library

[9]

D.I.K. Sjøberg, A. Johnsen, and J. Solberg (2012). Quantifying the effect of using Kanban versus Scrum: A case study. IEEE software, 29(5), 47--53.

Digital Library

[10]

D.I.K. Sjøberg, B. Anda, E. Arisholm, T. Dybå, M. Jørgensen, A. Karahasanovic, E. Koren, and M. Vokác. 2002. Conducting realistic experiments in software engineering. In International Symposium on Empirical Software Engineering (ISESE). IEEE, 17--26.

Digital Library

[11]

L.J. Cronbach and P.E. Meehl (1955). Construct validity in psychological tests. Psychological bulletin, 52(4), 281.

[12]

D.I.K. Sjøberg, A. Yamashita, B.C.D. Anda, A. Mockus, and T. Dybå (2013). Quantifying the effect of code smells on maintenance effort. IEEE Transactions on Software Engineering, 39(8), 1144--1156.

Digital Library

[13]

T. Skeie. 2014. Does limit on Work-In-Progress (WIP) in software development matter? MSc thesis. Department of Informatics, University of Oslo, Norway.

[14]

G.R. Bergersen, J.E. Hannay, D.I.K. Sjøberg, T. Dybå, and A. Karahasanovic. 2011. Inferring skill from tests of programming performance: Combining time and quality. In International Symposium on Empirical Software Engineering and Measurement (ESEM). IEEE, 305--314.

Digital Library

[15]

S. Holm (1979). A simple sequentially rejective multiple test procedure. Scandinavian journal of statistics, 65--70.

[16]

T.V. Perneger (1998). What's wrong with Bonferroni adjustments. Bmj, 316(7139), 1236--1238.

[17]

R.A. Armstrong (2014). When to use the Bonferroni correction. Ophthalmic and Physiological Optics, 34(5), 502--508.

[18]

Y. Lindsjørn, D.I.K. Sjøberg, T. Dingsøyr, G.R. Bergersen, and T. Dybå (2016). Teamwork quality and project success in software development: A survey of agile development teams. Journal of Systems and Software, 122, 274--286.

Digital Library

[19]

G.R. Bergersen, D.I.K. Sjøberg, and T. Dybå (2014). Construction and validation of an instrument for measuring programming skill. IEEE Transactions on Software Engineering, 40(12), 1163--1184.

[20]

V.G. Stray, N.B. Moe, and T. Dingsøyr. 2011. Challenges to teamwork: a multiple case study of two agile teams. In International Conference on Agile Software Development. Springer, Berlin, Heidelberg, 146--161.

[21]

T. Dybå, D.I.K. Sjøberg, and D.S. Cruzes. 2012. What works for whom, where, when, and why? On the role of context in empirical software engineering. In International Symposium on Empirical Software Engineering and Measurement (ESEM). ACM-IEEE, 19--28.

Digital Library

[22]

B.C.D. Anda, D.I.K. Sjøberg, and A. Mockus (2009). Variability and reproducibility in software engineering: A study of four companies that developed the same system. IEEE Transactions on Software Engineering, 35(3), 407--429.

Digital Library

Cited By

Damij NDamij T(2024)An Approach to Optimizing Kanban Board Workflow and Shortening the Project Management PlanIEEE Transactions on Engineering Management10.1109/TEM.2021.312098471(13266-13273)Online publication date: 2024
https://doi.org/10.1109/TEM.2021.3120984
Guerrero-Calvache MHernández G(2023)Team Productivity Factors in Agile Software Development: An Exploratory Survey with PractitionersApplied Informatics10.1007/978-3-031-46813-1_18(261-276)Online publication date: 24-Oct-2023
https://doi.org/10.1007/978-3-031-46813-1_18
Leal SHauck JBertan MVieira G(2022)How Agile Organizations Use Metrics: A Systematic Literature MappingProceedings of the XXI Brazilian Symposium on Software Quality10.1145/3571473.3571479(1-11)Online publication date: 7-Nov-2022
https://dl.acm.org/doi/10.1145/3571473.3571479

Index Terms

An empirical study of WIP in kanban teams
1. Software and its engineering
  1. Software creation and management
    1. Software development process management

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cover image ACM Conferences

ESEM '18: Proceedings of the 12th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement

October 2018

487 pages

ISBN:9781450358231

DOI:10.1145/3239235

General Chair:
Markku Oivo
University of Oulu, Finland
,
Program Chairs:
Daniel Méndez
Technical University of Munich, Germany
,
Audris Mockus
University of Tennessee

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 11 October 2018

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ESEM '18: ACM / IEEE International Symposium on Empirical Software Engineering and Measurement

October 11 - 12, 2018

Oulu, Finland

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Cited By

Damij NDamij T(2024)An Approach to Optimizing Kanban Board Workflow and Shortening the Project Management PlanIEEE Transactions on Engineering Management10.1109/TEM.2021.312098471(13266-13273)Online publication date: 2024
https://doi.org/10.1109/TEM.2021.3120984
Guerrero-Calvache MHernández G(2023)Team Productivity Factors in Agile Software Development: An Exploratory Survey with PractitionersApplied Informatics10.1007/978-3-031-46813-1_18(261-276)Online publication date: 24-Oct-2023
https://doi.org/10.1007/978-3-031-46813-1_18
Leal SHauck JBertan MVieira G(2022)How Agile Organizations Use Metrics: A Systematic Literature MappingProceedings of the XXI Brazilian Symposium on Software Quality10.1145/3571473.3571479(1-11)Online publication date: 7-Nov-2022
https://dl.acm.org/doi/10.1145/3571473.3571479
Guerrero-Calvache MHernández G(2022)Team Productivity in Agile Software Development: A Systematic Mapping StudyApplied Informatics10.1007/978-3-031-19647-8_32(455-471)Online publication date: 19-Oct-2022
https://doi.org/10.1007/978-3-031-19647-8_32

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