(An) empirical analysis of software effort estimation with outlier elimination = 이상치 데이터의 제거 기법을 이용한 소프트웨어 공수 예측 모델의 경험적 분석

Accurate estimation has always been challenge for software engineering communities. Many researches have done studies where estimation models were compared to choose the best accurate model or new estimation models were proposed to improve the prediction accuracy. However, many works did not consider the data set which we believe is a basis to build accurate estimation model. The data set often has a faulty, incomplete data and extreme value data. Such data is called an outlier. Therefore, the outlier need to be handled to build a better model. In this thesis, we investigate the prediction accuracy of effort estimation models when applying outlier elimination techniques. Three commonly used effort estimation models, and two outlier elimination techniques are selected for our empirical study. The empirical results show that the prediction accuracy of effort estimation models with outlier elimination techniques are more accurate than that of effort estimation models which is not applied the outlier elimination techniques. In addition, our study shows different result depend on the models based on two different data samples. Our study can be used in organizations to build effort estimation model for current or future projects.
Advisors
Bae, Doo-Hwanresearcher배두환researcher
Publisher
한국과학기술원
Issue Date
2008
Identifier
297248/325007  / 020063243
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 전산학전공, 2008.2, [ vi, 47 p. ]

Keywords

Effort estimation; Outlier elimination; 공수 예측; 이상치 데이터의 제거; Effort estimation; Outlier elimination; 공수 예측; 이상치 데이터의 제거

URI
http://hdl.handle.net/10203/34803
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=297248&flag=t
Appears in Collection
CS-Theses_Master(석사논문)
Files in This Item
There are no files associated with this item.
  • Hit : 417
  • Download : 0
  • Cited 0 times in thomson ci

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0