SEL@KIT: M. Kondo, O. Mizuno, and E. Choi, Causal-Effect Analysis Using Bayesian Lingam Comparing with Correlation Analysis in Function Point Metrics and Effort, 2018.
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M. Kondo, O. Mizuno, and E. Choi, "Causal-Effect Analysis Using Bayesian Lingam Comparing with Correlation Analysis in Function Point Metrics and Effort," International Journal of Mathematical, Engineering and Management Sciences (IJMEMS), 3(2), 90–112, 2018.
ID 758
分類 学術論文誌(査読付)
タグ analysis bayesian causal-effect comparing correlation effort function lingam metrics point
表題 (title) Causal-Effect Analysis Using Bayesian Lingam Comparing with Correlation Analysis in Function Point Metrics and Effort
表題 (英文)
著者名 (author) Masanari Kondo,Osamu Mizuno,Eun-Hye Choi
英文著者名 (author) Masanari Kondo,Osamu Mizuno,Eun-Hye Choi
キー (key) Masanari Kondo,Osamu Mizuno,Eun-Hye Choi
定期刊行物名 (journal) International Journal of Mathematical, Engineering and Management Sciences (IJMEMS)
定期刊行物名 (英文)
巻数 (volume) 3
号数 (number) 2
ページ範囲 (pages) 90–112
刊行月 (month) 0
出版年 (year) 2018
Impact Factor (JCR)
URL http://www.ijmems.in/assets/8-vol.-3%2C-no.-2%2C-90–112%2C-2018.pdf
付加情報 (note) ISSN: 2455-7749
注釈 (annote)
内容梗概 (abstract) Software effort estimation is a critical task for successful software development, which is necessary for appropriately managing software task assignment and schedule and consequently producing high-quality software. Function point (FP) metrics are commonly used for software effort estimation. To build a good effort estimation model, independent explanatory variables corresponding to FP metrics are required to avoid a multicollinearity problem. For this reason, previous studies have tackled analyzing correlation relationships between FP metrics. However, previous results on the relationships have some inconsistencies. To obtain evidence for such inconsistent results and achieve more effective effort estimation, we propose a novel analysis, which investigates causal-effect relationships between FP metrics and effort. We use an advanced linear non-Gaussian acyclic model called BayesLiNGAM for our causal-effect analysis, and compare the correlation relationships with the causal-effect relationships between FP metrics. In this paper, we report several new findings including the most effective FP metric for effort estimation investigated by our analysis using two datasets.
論文電子ファイル published (application/pdf) [一般閲覧可]
BiBTeXエントリ
@article{id758,
         title = {Causal-Effect Analysis using Bayesian LiNGAM Comparing with Correlation Analysis in Function Point Metrics and Effort},
        author = {Masanari Kondo and Osamu Mizuno and Eun-Hye Choi},
       journal = {International Journal of Mathematical, Engineering and Management Sciences (IJMEMS)},
        volume = {3},
        number = {2},
         pages = {90–112},
         month = {0},
          year = {2018},
          note = {ISSN: 2455-7749},
}
  

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