Data Smoothing for Software Effort Estimation

Kento Korenaga, Akito Monden, Zeynep Yucel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The goal of this paper is to improve the estimation performance of software development effort by mitigating the problem caused by outliers in a historical software project data set, which is used to construct an effort estimation model. To date, outlier removal methods have been proposed to solve this problem; however, they are not always effective because removing outliers reduces the number of data points (= software projects in our case) in a data set, and a model built from a small data set often suffers from lack of generality. In such a case, estimation performance can become even worse. In this paper we propose a method called data smoothing to mitigate the problem of outliers without reducing the number of data points. We consider that data points are outliers if they do not meet the assumption of Analogy-Based Estimation (ABE) such that 'projects with similar features require similar development efforts.' The proposed method changes the effort values (person-months or person-hours) in a data set so as to satisfy this assumption; and by this way, all outliers become non-outliers without decreasing the data points. As a result of experimental evaluation using 8 software development data sets, we found that the proposed data smoothing showed the same or higher effort estimation accuracy than the non-smoothing case, while conventional outlier removal method showed worse accuracy in some data set.

Original languageEnglish
Title of host publicationProceedings - 20th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2019
EditorsMasahide Nakamura, Hiroaki Hirata, Takayuki Ito, Takanobu Otsuka, Shun Okuhara
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages501-506
Number of pages6
ISBN (Electronic)9781728116518
DOIs
Publication statusPublished - Jul 2019
Event20th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2019 - Toyama, Japan
Duration: Jul 8 2019Jul 11 2019

Publication series

NameProceedings - 20th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2019

Conference

Conference20th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2019
CountryJapan
CityToyama
Period7/8/197/11/19

Keywords

  • Software project planning
  • data preprocessing
  • outlier removal

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Software
  • Information Systems and Management

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