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Scrum Task Allocation Based on Particle Swarm Optimization

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Bioinspired Optimization Methods and Their Applications (BIOMA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10835))

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Abstract

In this paper, we present a novel algorithm called STAPSO, which comprises Scrum task allocation and the Particle Swarm Optimization algorithm. The proposed algorithm aims to address one of the most significant problems in the agile software development, i.e., iteration planning. The actuality of the topic is not questionable, since nowadays, agile software development plays a vital role in most of the organizations around the world. Despite many agile software development methodologies, we include the proposed algorithm in Scrum Sprint planning, as it is the most widely used methodology. The proposed algorithm was also tested on a real-world dataset, and the experiment shows promising results.

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Acknowledgment

The authors acknowledge the financial support from the Slovenian Research Agency (Research Core Funding No. P2-0057).

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Authors and Affiliations

  1. Institute of Informatics, Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, 2000, Maribor, Slovenia

    Lucija Brezočnik, Iztok Fister Jr. & Vili Podgorelec

Authors
  1. Lucija Brezočnik
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  2. Iztok Fister Jr.
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  3. Vili Podgorelec
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Corresponding author

Correspondence to Lucija Brezočnik .

Editor information

Editors and Affiliations

  1. Computer Systems Department, Jožef Stefan Institute, Ljubljana, Slovenia

    Peter Korošec

  2. University of Lille, Lille, France

    Nouredine Melab

  3. University of Lille, Lille, France

    El-Ghazali Talbi

Test Data

Test Data

Table 4. Test data
Full size table

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Brezočnik, L., Fister, I., Podgorelec, V. (2018). Scrum Task Allocation Based on Particle Swarm Optimization. In: Korošec, P., Melab, N., Talbi, EG. (eds) Bioinspired Optimization Methods and Their Applications. BIOMA 2018. Lecture Notes in Computer Science(), vol 10835. Springer, Cham. https://doi.org/10.1007/978-3-319-91641-5_4

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  • DOI: https://doi.org/10.1007/978-3-319-91641-5_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-91640-8

  • Online ISBN: 978-3-319-91641-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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