Investigating the Significance of Bellwether Effect to improve Software Effort Estimation

Solomon Mensah; Jacky Keung; Stephen G. MacDonell; Michael F. Bosu; Kwabena E. Bennin

doi:10.1109/QRS.2017.44

Investigating the Significance of Bellwether Effect to improve Software Effort Estimation

Solomon Mensah
, Jacky Keung
, Stephen G. MacDonell
, Michael F. Bosu
, Kwabena E. Bennin

Department of Computer Science

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

13 Citations (Scopus)

Abstract

Bellwether effect refers to the existence of exemplary projects (called the Bellwether) within a historical dataset to be used for improved prediction performance. Recent studies have shown an implicit assumption of using recently completed projects (referred to as moving window) for improved prediction accuracy. In this paper, we investigate the Bellwether effect on software effort estimation accuracy using moving windows. The existence of the Bellwether was empirically proven based on six postulations. We apply statistical stratification and Markov chain methodology to select the Bellwether moving window. The resulting Bellwether moving window is used to predict the software effort of a new project. Empirical results show that Bellwether effect exist in chronological datasets with a set of exemplary and recently completed projects representing the Bellwether moving window. Result from this study has shown that the use of Bellwether moving window with the Gaussian weighting function significantly improve the prediction accuracy.

Original language	English
Title of host publication	2017 IEEE International Conference on Software Quality, Reliability and Security (QRS)
Publisher	IEEE
Pages	340-351
ISBN (Electronic)	978-1-5386-0592-9
DOIs	https://doi.org/10.1109/QRS.2017.44
Publication status	Published - Aug 2017
Event	The 2017 IEEE International Conference on Software Quality, Reliability, and Security - Faculty of Information Technology, Czech Technical University, Prague, Czech Republic Duration: 25 Jul 2017 → 29 Jul 2017 Conference number: 17th http://paris.utdallas.edu/qrs17 http://paris.utdallas.edu/qrs17/ http://paris.utdallas.edu/qrs17/ http://paris.utdallas.edu/qrs17/program/QRS-2017-Program.pdf

Publication series

Name	IEEE International Conference on Software Quality, Reliability and Security (QRS)
Publisher	IEEE

Conference

Conference	The 2017 IEEE International Conference on Software Quality, Reliability, and Security
Abbreviated title	QRS 2017
Place	Czech Republic
City	Prague
Period	25/07/17 → 29/07/17
Internet address	http://paris.utdallas.edu/qrs17 http://paris.utdallas.edu/qrs17/ http://paris.utdallas.edu/qrs17/ http://paris.utdallas.edu/qrs17/program/QRS-2017-Program.pdf

Research Keywords

Bellwether Effect
Bellwether moving window
Chronological dataset
Markov chains

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10.1109/QRS.2017.44

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Mensah, S., Keung, J., MacDonell, S. G., Bosu, M. F., & Bennin, K. E. (2017). Investigating the Significance of Bellwether Effect to improve Software Effort Estimation. In 2017 IEEE International Conference on Software Quality, Reliability and Security (QRS) (pp. 340-351). Article 8009938 (IEEE International Conference on Software Quality, Reliability and Security (QRS)). IEEE. https://doi.org/10.1109/QRS.2017.44

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title = "Investigating the Significance of Bellwether Effect to improve Software Effort Estimation",

abstract = "Bellwether effect refers to the existence of exemplary projects (called the Bellwether) within a historical dataset to be used for improved prediction performance. Recent studies have shown an implicit assumption of using recently completed projects (referred to as moving window) for improved prediction accuracy. In this paper, we investigate the Bellwether effect on software effort estimation accuracy using moving windows. The existence of the Bellwether was empirically proven based on six postulations. We apply statistical stratification and Markov chain methodology to select the Bellwether moving window. The resulting Bellwether moving window is used to predict the software effort of a new project. Empirical results show that Bellwether effect exist in chronological datasets with a set of exemplary and recently completed projects representing the Bellwether moving window. Result from this study has shown that the use of Bellwether moving window with the Gaussian weighting function significantly improve the prediction accuracy.",

keywords = "Bellwether Effect, Bellwether moving window, Chronological dataset, Markov chains",

author = "Solomon Mensah and Jacky Keung and MacDonell, \{Stephen G.\} and Bosu, \{Michael F.\} and Bennin, \{Kwabena E.\}",

year = "2017",

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language = "English",

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booktitle = "2017 IEEE International Conference on Software Quality, Reliability and Security (QRS)",

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note = "The 2017 IEEE International Conference on Software Quality, Reliability, and Security, QRS 2017 ; Conference date: 25-07-2017 Through 29-07-2017",

url = "http://paris.utdallas.edu/qrs17, http://paris.utdallas.edu/qrs17/, http://paris.utdallas.edu/qrs17/, http://paris.utdallas.edu/qrs17/program/QRS-2017-Program.pdf",

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Mensah, S , Keung, J, MacDonell, SG, Bosu, MF & Bennin, KE 2017, Investigating the Significance of Bellwether Effect to improve Software Effort Estimation. in 2017 IEEE International Conference on Software Quality, Reliability and Security (QRS)., 8009938, IEEE International Conference on Software Quality, Reliability and Security (QRS), IEEE, pp. 340-351, The 2017 IEEE International Conference on Software Quality, Reliability, and Security, Prague, Czech Republic, 25/07/17. https://doi.org/10.1109/QRS.2017.44

Investigating the Significance of Bellwether Effect to improve Software Effort Estimation. / Mensah, Solomon ; Keung, Jacky; MacDonell, Stephen G. et al.
2017 IEEE International Conference on Software Quality, Reliability and Security (QRS). IEEE, 2017. p. 340-351 8009938 (IEEE International Conference on Software Quality, Reliability and Security (QRS)).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Investigating the Significance of Bellwether Effect to improve Software Effort Estimation

AU - Mensah, Solomon

AU - Keung, Jacky

AU - MacDonell, Stephen G.

AU - Bosu, Michael F.

AU - Bennin, Kwabena E.

N1 - Conference code: 17th

PY - 2017/8

Y1 - 2017/8

N2 - Bellwether effect refers to the existence of exemplary projects (called the Bellwether) within a historical dataset to be used for improved prediction performance. Recent studies have shown an implicit assumption of using recently completed projects (referred to as moving window) for improved prediction accuracy. In this paper, we investigate the Bellwether effect on software effort estimation accuracy using moving windows. The existence of the Bellwether was empirically proven based on six postulations. We apply statistical stratification and Markov chain methodology to select the Bellwether moving window. The resulting Bellwether moving window is used to predict the software effort of a new project. Empirical results show that Bellwether effect exist in chronological datasets with a set of exemplary and recently completed projects representing the Bellwether moving window. Result from this study has shown that the use of Bellwether moving window with the Gaussian weighting function significantly improve the prediction accuracy.

AB - Bellwether effect refers to the existence of exemplary projects (called the Bellwether) within a historical dataset to be used for improved prediction performance. Recent studies have shown an implicit assumption of using recently completed projects (referred to as moving window) for improved prediction accuracy. In this paper, we investigate the Bellwether effect on software effort estimation accuracy using moving windows. The existence of the Bellwether was empirically proven based on six postulations. We apply statistical stratification and Markov chain methodology to select the Bellwether moving window. The resulting Bellwether moving window is used to predict the software effort of a new project. Empirical results show that Bellwether effect exist in chronological datasets with a set of exemplary and recently completed projects representing the Bellwether moving window. Result from this study has shown that the use of Bellwether moving window with the Gaussian weighting function significantly improve the prediction accuracy.

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KW - Chronological dataset

KW - Markov chains

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M3 - RGC 32 - Refereed conference paper (with host publication)

T3 - IEEE International Conference on Software Quality, Reliability and Security (QRS)

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ER -

Mensah S , Keung J, MacDonell SG, Bosu MF, Bennin KE. Investigating the Significance of Bellwether Effect to improve Software Effort Estimation. In 2017 IEEE International Conference on Software Quality, Reliability and Security (QRS). IEEE. 2017. p. 340-351. 8009938. (IEEE International Conference on Software Quality, Reliability and Security (QRS)). doi: 10.1109/QRS.2017.44

Investigating the Significance of Bellwether Effect to improve Software Effort Estimation

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Research Keywords

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