Realistic assessment of software effort estimation models

Boyce Sigweni, Martin Shepperd, Tommaso Turchi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

Abstract

Context: It is unclear that current approaches to evaluating or comparing competing software cost or effort models give a realistic picture of how they would perform in actual use. Specifically, we're concerned that the usual practice of using all data with some holdout strategy is at variance with the reality of a data set growing as projects complete. Objective: This study investigates the impact of using unrealistic, though possibly convenient to the researchers, ways to compare models on commercial data sets. Our questions are does this lead to different conclusions in terms of the comparisons and if so,are the results biased e.g., more optimistic than those that might realistically be achieved in practice. Method: We compare a traditional approach based on leave one out cross-validation with growing the data set chronologically using the Finnish and Desharnais data sets. Results: Our realistic, time-based approach to validation is significantly more conservative than leave-one-out cross-validation (LOOCV) for both data sets. Conclusion: If we want our research to lead to actionable findings it's incumbent upon the researchers to evaluate their models in realistic ways. This means a departure from LOOCV techniques, while further investigation is needed for other validation techniques, such as k-fold validation.

Original languageEnglish
Title of host publicationProceedings of the 20th International Conference on Evaluation and Assessment in Software Engineering, EASE 2016
PublisherAssociation for Computing Machinery
Volume01-03-June-2016
ISBN (Electronic)9781450336918
DOIs
Publication statusPublished - Jun 1 2016
Event20th International Conference on Evaluation and Assessment in Software Engineering, EASE 2016 - Limerick, Ireland
Duration: Jun 1 2016Jun 3 2016

Other

Other20th International Conference on Evaluation and Assessment in Software Engineering, EASE 2016
CountryIreland
CityLimerick
Period6/1/166/3/16

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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  • Cite this

    Sigweni, B., Shepperd, M., & Turchi, T. (2016). Realistic assessment of software effort estimation models. In Proceedings of the 20th International Conference on Evaluation and Assessment in Software Engineering, EASE 2016 (Vol. 01-03-June-2016). [a41] Association for Computing Machinery. https://doi.org/10.1145/2915970.2916005