TY - GEN
T1 - A review and comparison of methods for determining the best analogies in analogy-based software effort estimation
AU - Chinthanet, Bodin
AU - Leelaprute, Pattara
AU - Rungsawang, Arnon
AU - Phannachitta, Passakorn
AU - Ubayashi, Naoyasu
AU - Kamei, Yasutaka
AU - Matsumoto, Kenichi
N1 - Funding Information:
The first author would like to thank both Kasetsart University and NAIST-Japan for giving him a good opportunity to be included in KU-NAIST internship 2015 program.
Publisher Copyright:
© 2016 ACM.
PY - 2016/4/4
Y1 - 2016/4/4
N2 - Analogy-based effort estimation (ABE) is a commonly used software development effort estimation method. The processes of ABE are based on a reuse of effort values from similar past projects, where the appropriate numbers of past projects (k values) to be reused is one of the long-standing debates in ABE research studies. To date, many approaches to find this k value have been continually proposed. One important reason for this inconclusive debate is that different studies appear to produce different conclusions of the k value to be appropriate. Therefore, in this study, we revisit 8 common approaches to the k value being most appropriate in general situations. With a more robust and comprehensive evaluation methodology using 5 robust error measures subject to the Wilcoxon rank-sum statistical test, we found that conflicting results in the previous studies were not mainly due to the use of different methodologies nor different datasets, but the performance of the different approaches are actually varied widely.
AB - Analogy-based effort estimation (ABE) is a commonly used software development effort estimation method. The processes of ABE are based on a reuse of effort values from similar past projects, where the appropriate numbers of past projects (k values) to be reused is one of the long-standing debates in ABE research studies. To date, many approaches to find this k value have been continually proposed. One important reason for this inconclusive debate is that different studies appear to produce different conclusions of the k value to be appropriate. Therefore, in this study, we revisit 8 common approaches to the k value being most appropriate in general situations. With a more robust and comprehensive evaluation methodology using 5 robust error measures subject to the Wilcoxon rank-sum statistical test, we found that conflicting results in the previous studies were not mainly due to the use of different methodologies nor different datasets, but the performance of the different approaches are actually varied widely.
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U2 - 10.1145/2851613.2851974
DO - 10.1145/2851613.2851974
M3 - Conference contribution
AN - SCOPUS:84975883812
T3 - Proceedings of the ACM Symposium on Applied Computing
SP - 1554
EP - 1557
BT - 2016 Symposium on Applied Computing, SAC 2016
PB - Association for Computing Machinery
T2 - 31st Annual ACM Symposium on Applied Computing, SAC 2016
Y2 - 4 April 2016 through 8 April 2016
ER -