Multi-objective Software Effort Estimation

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@InProceedings{Sarro:2016:ICSE,
  author =       "Federica Sarro and Alessio Petrozziello and 
                 Mark Harman",
  title =        "Multi-objective Software Effort Estimation",
  booktitle =    "Proceedings of the 38th International Conference on
                 Software Engineering, ICSE '16",
  year =         "2016",
  pages =        "619--630",
  address =      "Austin, Texas, USA",
  publisher_address = "New York, NY, USA",
  publisher =    "ACM",
  note =         "Humie 2016 Bronze Medal",
  keywords =     "genetic algorithms, genetic programming, SBSE,
                 NSGA-II, confidence interval, estimates uncertainty,
                 multi-objective evolutionary algorithm, software effort
                 estimation",
  isbn13 =       "978-1-4503-3900-1",
  acmid =        "2884830",
  URL =          "http://www.cs.ucl.ac.uk/staff/F.Sarro/resource/papers/SarroICSE2016.pdf",
  URL =          "http://doi.acm.org/10.1145/2884781.2884830",
  DOI =          "doi:10.1145/2884781.2884830",
  size =         "12",
  abstract =     "We introduce a bi-objective effort estimation
                 algorithm that combines Confidence Interval Analysis
                 and assessment of Mean Absolute Error. We evaluate our
                 proposed algorithm on three different alternative
                 formulations, baseline comparators and current
                 state-of-the-art effort estimators applied to five
                 real-world datasets from the PROMISE repository,
                 involving 724 different software projects in total. The
                 results reveal that our algorithm outperforms the
                 baseline, state-of-the-art and all three alternative
                 formulations, statistically significantly and with
                 large effect size over all five datasets. We also
                 provide evidence that our algorithm creates a new
                 state-of-the-art, which lies within currently claimed
                 industrial human-expert-based thresholds, thereby
                 demonstrating that our findings have actionable
                 conclusions for practising software engineers.",
  notes =        "Linear genome

                 Also known as \cite{Sarro:2016:MSE:2884781.2884830}",
}

Genetic Programming entries for Federica Sarro Alessio Petrozziello Mark Harman

Citations