Effort estimation based on collaborative filtering

Naoki Ohsugi, Masateru Tsunoda, Akito Monden, Ken Ichi Matsumoto

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

Effort estimation methods are one of the important tools for project managers in controlling human resources of ongoing or future software projects. The estimations require historical project data including process and product metrics that characterize past projects. Practically, in using the estimation methods, it is a problem that the historical project data frequently contain substantial missing values. In this paper, we propose an effort estimation method based on Collaborative Filtering for solving the problem. Collaborative Filtering has been developed in information retrieval researchers, as one of the estimation techniques using defective data, i.e. data having substantial missing values. The proposed method first evaluates similarity between a target (ongoing) project and each past project, using vector based similarity computation equation. Then it predicts the effort of the target project with the weighted sum of the efforts of past similar projects. We conducted an experimental case study to evaluate the estimation performance of the proposed method. The proposed method showed better performance than the conventional regression method when the data had substantial missing values.

Original languageEnglish
Pages (from-to)274-286
Number of pages13
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3009
Publication statusPublished - 2004
Externally publishedYes

Fingerprint

Effort Estimation
Collaborative filtering
Collaborative Filtering
Missing Values
Human Resources
Target
Information retrieval
Evaluate
Information Storage and Retrieval
Weighted Sums
Information Retrieval
Managers
Personnel
Software
Regression
Research Personnel
Metric
Predict

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

Effort estimation based on collaborative filtering. / Ohsugi, Naoki; Tsunoda, Masateru; Monden, Akito; Matsumoto, Ken Ichi.

In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 3009, 2004, p. 274-286.

Research output: Contribution to journalArticle

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