Counselor, a Data Mining Based Time Estimation for Software Maintenance

  • Authors:

  • Hércules Antonio Prado;Edilson Ferneda;Nicolas Anquetil;Elizabeth D'Arrochella Teixeira

  • Affiliations:

  • Catholic University of Brasília, Brazil and EMBRAPA Management and Strategy, Brasília, Brazil;Catholic University of Brasília, Brazil;Catholic University of Brasília, Brazil;Politec S.A., Brazil

  • Venue:
  • KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II
  • Year:
  • 2009

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Abstract

Measuring and estimating are fundamental activities for the success of any project. In the software maintenance realm the lack of maturity, or even a low level of interest in adopting effective maintenance techniques and related metrics, have been pointed out as an important cause for the high costs involved. In this paper data mining techniques are applied to provide a sound estimation for the time required to accomplish a maintenance task. Based on real world data regarding maintenance requests, some regression models are built to predict the time required for each maintenance. Data on the team skill and the maintenance characteristics are mapped into values that predict better time estimations in comparison to the one predicted by the human expert. A particular finding from this research is that the time prediction provided by a human expert works as an inductive bias that improves the overall prediction accuracy.