Journal of Software: Evolution and Process

Cover image for Vol. 27 Issue 7

Special Issue: Software Productivity and Effort Estimation

July 2015

Volume 27, Issue 7

Pages i–iii, 465–507

Issue edited by: Jens Heidrich, Markku Oivo, Andreas Jedlitschka

  1. Issue Information

    1. Top of page
    2. Issue Information
    3. Editorial
    4. Special Issue Papers
    1. Issue Information (pages i–iii)

      Article first published online: 14 JUL 2015 | DOI: 10.1002/smr.1685

  2. Editorial

    1. Top of page
    2. Issue Information
    3. Editorial
    4. Special Issue Papers
    1. You have free access to this content
      Software productivity and effort estimation (pages 465–466)

      Jens Heidrich, Markku Oivo and Andreas Jedlitschka

      Article first published online: 26 JUN 2015 | DOI: 10.1002/smr.1722

  3. Special Issue Papers

    1. Top of page
    2. Issue Information
    3. Editorial
    4. Special Issue Papers
    1. Do feelings matter? On the correlation of affects and the self-assessed productivity in software engineering (pages 467–487)

      Daniel Graziotin, Xiaofeng Wang and Pekka Abrahamsson

      Article first published online: 6 AUG 2014 | DOI: 10.1002/smr.1673

      Thumbnail image of graphical abstract

      This article provides basic theoretical building blocks on researching the human side of software development in empirical software engineering with psychological measurements. It examines the correlation of the affects and the performance of software developers working in natural settings on real-world software projects. The results show that the real-time affects related to a software development task are positively correlated with a programmer's self-assessed productivity. This article highlights the body of knowledge in psychology and management research on the affects and their impact on performance.

    2. On the effectiveness of weighted moving windows: Experiment on linear regression based software effort estimation (pages 488–507)

      S. Amasaki and C. Lokan

      Article first published online: 20 AUG 2014 | DOI: 10.1002/smr.1672

      Thumbnail image of graphical abstract

      On the effectiveness of weighted moving windows: Experiment on linear regression based software effort estimation Authors: S. Amasaki and C. Lokan mini-abst(80words): It seems effective to use a window of training data so that an effort estimation model is trained with only recent projects. Considering the chronological order of projects within the window, and weighting projects according to their order within the window, may also affect estimation accuracy. We examined the effects of weighted moving windows on effort estimation accuracy. We confirmed that weighting methods significantly improved estimation accuracy in larger windows, though the methods also significantly worsened accuracy in smaller windows.

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