Effort Prediction Model Using Similarity for Embedded Software Development

  • Kazunori Iwata
  • Yoshiyuki Anan
  • Toyoshiro Nakashima
  • Naohiro Ishii
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3981)


In this paper, we propose an effort prediction model in which data including missing values is complemented by using the collaborative filtering [1, 2, 3] and the effort of projects is derived from a multiple regression analysis [4, 5] using the data. Because companies, recently, focus on methods to predict effort of projects, which prevent project failures such as exceeding deadline and cost, due to more complex embedded software, which brings the evolution of the performance and function enhancement [6, 7, 8]. Moreover, we conduct the evaluation experiment that compared the accuracy of our method with other two methods according to five criteria to confirm their accuracy. The results of the experiment shows that our method gives predictions the best in the five evaluation criteria.


Multiple Regression Analysis Recommendation System User Evaluation Project Data Collaborative Filter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kazunori Iwata
    • 1
  • Yoshiyuki Anan
    • 2
  • Toyoshiro Nakashima
    • 3
  • Naohiro Ishii
    • 4
  1. 1.Department of Business AdministrationAichi UniversityAichiJapan
  2. 2.Business Innovation Driving Department, SPI Driving Group, Planning OfficeOmron Software Co., Ltd.KyotoJapan
  3. 3.Department of Culture-Information StudiesSugiyama Jogakuen UniversityNagoya, AichiJapan
  4. 4.Department of Marketing and Information SystemsAichi Institute of TechnologyToyota, AichiJapan

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