Analysis on Causal-Effect Relationship in Effort Metrics Using Bayesian LiNGAM

Masanari Kondo, Osamu Mizuno

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

1 Citation (Scopus)

Abstract

In the effort estimation studies, we can obtain open datasets from the past research. Those datasets are either within-company or cross-company dataset. On effort estimation, it was long discussed which dataset is appropriate for building accurate model. To find a new viewpoint in this discussion, we introduce the causal-effect relationship estimation technique. We use a simple Bayesian approach that is defined by the data generation model in a Linear Non-Gaussian Acyclic Model (LiNGAM). This model is applied to the function point and effort metrics in both within-company and cross-company datasets. We assume that if a dataset is appropriate for effort estimation, causal-effect relationships between metrics and effort will be extracted more. The result of case study shows that we can extract more causal-effect relationships from the cross-company dataset than that of from the within-company dataset.

Original languageEnglish
Title of host publicationProceedings - 2016 IEEE 27th International Symposium on Software Reliability Engineering Workshops, ISSREW 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages47-48
Number of pages2
ISBN (Electronic)9781509036011
DOIs
Publication statusPublished - Dec 16 2016
Externally publishedYes
Event27th IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2016 - Ottawa, Canada
Duration: Oct 23 2016Oct 27 2016

Publication series

NameProceedings - 2016 IEEE 27th International Symposium on Software Reliability Engineering Workshops, ISSREW 2016

Conference

Conference27th IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2016
CountryCanada
CityOttawa
Period10/23/1610/27/16

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality
  • Software

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