Genetic Programming for Effort Estimation: An Analysis of the Impact of Different Fitness Functions

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{FerrucciGOS10,
  author =       "Filomena Ferrucci and Carmine Gravino and 
                 Rocco Oliveto and Federica Sarro",
  title =        "Genetic Programming for Effort Estimation: An Analysis
                 of the Impact of Different Fitness Functions",
  booktitle =    "Proceedings of the 2nd International Symposium on
                 Search Based Software Engineering (SSBSE '10)",
  year =         "2010",
  pages =        "89--98",
  address =      "Benevento, Italy",
  month =        "7-9 " # sep,
  publisher =    "IEEE",
  editor =       "Massimiliano {Di Penta} and Simon Poulding and 
                 Lionel Briand and John Clark",
  keywords =     "genetic algorithms, genetic programming, SBSE",
  isbn13 =       "978-0-7695-4195-2",
  DOI =          "doi:10.1109/SSBSE.2010.20",
  owner =        "Yuanyuan",
  timestamp =    "2010.09.08",
  size =         "10 pages",
  abstract =     "Context: The use of search-based methods has been
                 recently proposed for software development effort
                 estimation and some case studies have been carried out
                 to assess the effectiveness of Genetic Programming
                 (GP). The results reported in the literature showed
                 that GP can provide an estimation accuracy comparable
                 or slightly better than some widely used techniques and
                 encouraged further research to investigate whether
                 varying the fitness function the estimation accuracy
                 can be improved. Aim: Starting from these
                 considerations, in this paper we report on a case study
                 aiming to analyse the role played by some fitness
                 functions for the accuracy of the estimates. Method: We
                 performed a case study based on a publicly available
                 dataset, i.e., Desharnais, by applying a 3-fold cross
                 validation and employing summary measures and
                 statistical tests for the analysis of the results.
                 Moreover, we compared the accuracy of the obtained
                 estimates with those achieved using some widely used
                 estimation methods, namely Case-Based Reasoning (CBR)
                 and Manual Step Wise Regression (MSWR). Results: The
                 obtained results highlight that the fitness function
                 choice significantly affected the estimation accuracy.
                 The results also revealed that GP provided
                 significantly better estimates than CBR and comparable
                 with those of MSWR for the considered dataset.",
  notes =        "IEEE Computer Society Order Number P4195 BMS Part
                 Number: CFP1099G-PRT Library of Congress Number
                 2010933544 http://ssbse.org/2010/program.php",
}

Genetic Programming entries for Filomena Ferrucci Carmine Gravino Rocco Oliveto Federica Sarro

Citations