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Multi-agent Simulation of Agile Team Dynamics: Experiments on Team Strategies Comparisons

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Software Engineering Application in Informatics (CoMeSySo 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 232))

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Abstract

Scrum is an agile process that incrementally, iteratively and continuously delivers software based on sprints. It is comprised of user stories stored in product backlogs and delivered through sprints by a Scrum team consisting of developers, a Scrum Master and a Product Owner. The performance of a Scrum team is largely dependent on the capabilities of team members and the technical practices they adopt. One such practice, pair programming has been studied in a variety of contexts but not extensively in a Scrum context. Pair programming is a technique where two programmers work side by side to design, code, and test their program.

A multi agent system is used to simulate a scrum environment where a team (with varying team members’ capability) work on delivering user stories (which consists of multiple tasks with varying complexities) in multiple sprints. Using this simulated environment, various strategies of compulsory pairing and voluntary pairing are investigated. Impact is measured based on the team’s work efficiency, completion time, effort time and idle time.

Experiments were performed to test these strategies in varying environments and results showed that a hybrid pairing strategy performed the best in fixed environments as it avoided negative pairing situations. An adaptive strategy (which changes strategy depending of the composition of the team and the tasks to be completed) performed best in the random setting as it was able to use the best strategy based on the current environment.

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Notes

  1. 1.

    This is a slight variation from the Scrum setting where developers (including the Scrum Master) negotiate on which tasks they want to work on. In this implementation, we have made Scrum Master a coordinator agent to reduce communication threads between all the agents. However, all tasks are allocated based on the agents’ preferences.

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Acknowledgments

Sincerely, Thanks for Dr Patricia Anthony and Dr. Stuart Charters at Lincoln University, New Zealand to support this PhD research, also thanks for Pro. Guojian Cheng at Xi’an Petroleum University, China. Dr Gang Li at Deakin University, Australia and Professor Longbing Cao at University of Technology Sydney provides funding in the related data analysis and machine learning research which I was doing my invited research at UTS, Australia. I also thanks to Edinburgh Napier University, United Kingdom where I get my MSc in Advanced Software Engineering. They are all my best Supervisors support me to growth and become more and more professional.

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  1. Lincoln University, Lincoln, New Zealand

    Zhe Wang

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  1. Zhe Wang
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  1. Faculty of Applied Informatics, Tomas Bata University in ZlĂ­n, ZlĂ­n, Czech Republic

    Radek Silhavy

  2. Faculty of Applied Informatics, Tomas Bata University in ZlĂ­n, ZlĂ­n, Czech Republic

    Petr Silhavy

  3. Faculty of Applied Informatics, Tomas Bata University in ZlĂ­n, ZlĂ­n, Czech Republic

    Zdenka Prokopova

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Wang, Z. (2021). Multi-agent Simulation of Agile Team Dynamics: Experiments on Team Strategies Comparisons. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Software Engineering Application in Informatics. CoMeSySo 2021. Lecture Notes in Networks and Systems, vol 232. Springer, Cham. https://doi.org/10.1007/978-3-030-90318-3_24

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