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Effort and Cost Estimation Using Decision Tree Techniques and Story Points in Agile Software Development

Author

Listed:
  • Eduardo Rodríguez Sánchez

    (Universidad Autónoma Metropolitana Iztapalapa, Ciudad de México 09310, Mexico)

  • Eduardo Filemón Vázquez Santacruz

    (Universidad Autónoma Metropolitana Iztapalapa, Ciudad de México 09310, Mexico
    These authors contributed equally to this work.)

  • Humberto Cervantes Maceda

    (Universidad Autónoma Metropolitana Iztapalapa, Ciudad de México 09310, Mexico
    These authors contributed equally to this work.)

Abstract

Early effort estimation is important for efficiently planning the use of resources in an Information Technology (IT) project. However, limited research has been conducted on the topic of effort estimation in agile software development using artificial intelligence. This research project contributes to strengthening the use of hybrid models composed of algorithmic models and learning oriented techniques as a project-level effort estimation method in agile frameworks. Effort estimation in agile methods such as Scrum uses a story point approach that measures, using an arithmetic scale, the effort required to complete a release of the system. This project relied on labeled historical data to estimate the completion time measured in days and the total cost of a project set in Pakistani rupees (PKR). using a decision tree, random forest and AdaBoost to improve the accuracy of predictions. Models were trained using 10-fold cross-validation and the relative error was used as a comparison with literature results. The bootstrap aggregation (bagging) ensemble made of the three techniques provides the highest accuracy, and project classification also improves the estimates.

Suggested Citation

  • Eduardo Rodríguez Sánchez & Eduardo Filemón Vázquez Santacruz & Humberto Cervantes Maceda, 2023. "Effort and Cost Estimation Using Decision Tree Techniques and Story Points in Agile Software Development," Mathematics, MDPI, vol. 11(6), pages 1-31, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1477-:d:1100689
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    References listed on IDEAS

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    1. Wei-Yin Loh, 2014. "Fifty Years of Classification and Regression Trees," International Statistical Review, International Statistical Institute, vol. 82(3), pages 329-348, December.
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