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Propose-Specific Information Related to Prediction Level at x and Mean Magnitude of Relative Error: A Case Study of Software Effort Estimation

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  • Hoc Huynh Thai

    (Faculty of Applied Informatics, Tomas Bata University in Zlin, Nad Stranemi 4511, 76001 Zlin, Czech Republic
    Faculty of Information Technology, School of Engineering and Technology, Van Lang University, Ho Chi Minh City 700000, Vietnam
    These authors contributed equally to this work.)

  • Petr Silhavy

    (Faculty of Applied Informatics, Tomas Bata University in Zlin, Nad Stranemi 4511, 76001 Zlin, Czech Republic
    These authors contributed equally to this work.)

  • Martin Fajkus

    (Faculty of Applied Informatics, Tomas Bata University in Zlin, Nad Stranemi 4511, 76001 Zlin, Czech Republic
    These authors contributed equally to this work.)

  • Zdenka Prokopova

    (Faculty of Applied Informatics, Tomas Bata University in Zlin, Nad Stranemi 4511, 76001 Zlin, Czech Republic
    These authors contributed equally to this work.)

  • Radek Silhavy

    (Faculty of Applied Informatics, Tomas Bata University in Zlin, Nad Stranemi 4511, 76001 Zlin, Czech Republic
    These authors contributed equally to this work.)

Abstract

The prediction level at x ( P R E D ( x ) ) and mean magnitude of relative error ( M M R E ) are measured based on the magnitude of relative error between real and predicted values. They are the standard metrics that evaluate accurate effort estimates. However, these values might not reveal the magnitude of over-/under-estimation. This study aims to define additional information associated with the P R E D ( x ) and M M R E to help practitioners better interpret those values. We propose the formulas associated with the P R E D ( x ) and M M R E to express the level of scatters of predictive values versus actual values on the left ( s i g L e f t ), on the right ( s i g R i g h t ), and on the mean of the scatters ( s i g ). We depict the benefit of the formulas with three use case points datasets. The proposed formulas might contribute to enriching the value of the P R E D ( x ) and M M R E in validating the effort estimation.

Suggested Citation

  • Hoc Huynh Thai & Petr Silhavy & Martin Fajkus & Zdenka Prokopova & Radek Silhavy, 2022. "Propose-Specific Information Related to Prediction Level at x and Mean Magnitude of Relative Error: A Case Study of Software Effort Estimation," Mathematics, MDPI, vol. 10(24), pages 1-14, December.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:24:p:4649-:d:997383
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    References listed on IDEAS

    as
    1. Gneiting, Tilmann, 2011. "Making and Evaluating Point Forecasts," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 746-762.
    2. Radek Silhavy & Petr Silhavy & Zdenka Prokopova, 2015. "Algorithmic Optimisation Method for Improving Use Case Points Estimation," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-14, November.
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    1. Naledzani Ndou & Kgabo Humphrey Thamaga & Yonela Mndela & Adolph Nyamugama, 2023. "Radiometric Compensation for Occluded Crops Imaged Using High-Spatial-Resolution Unmanned Aerial Vehicle System," Agriculture, MDPI, vol. 13(8), pages 1-20, August.

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