TY - JOUR

T1 - A statistical methodology to simplify software metric models constructed using incomplete data samples

AU - Chan, Victor K.Y.

AU - Wong, W. Eric

AU - Xie, T. F.

N1 - Funding Information:
This paper is based on work supported by grant 045/2005/A from the Science and Technology Development Fund of the Government of the Macau Special Administrative Region, China.

PY - 2007/12

Y1 - 2007/12

N2 - Software metric models predict the target software metric(s), e.g., the development work effort or defect rates, for any future software project based on the project's predictor software metric(s), e.g., the project team size. Obviously, the construction of such a software metric model makes use of a data sample of such metrics from analogous past projects. However, incomplete data often appear in such data samples. Moreover, the decision on whether a particular predictor metric should be included is most likely based on an intuitive or experience-based assumption that the predictor metric has an impact on the target metric with a statistical significance. However, this assumption is usually not verifiable "retrospectively" after the model is constructed, leading to redundant predictor metric(s) and/or unnecessary predictor metric complexity. To solve all these problems, we derived a methodology consisting of the k-nearest neighbors (k-NN) imputation method, statistical hypothesis testing, and a "goodness-of-fit" criterion. This methodology was tested on software effort metric models and software quality metric models, the latter usually suffers from far more serious incomplete data. This paper documents this methodology and the tests on these two types of software metric models.

AB - Software metric models predict the target software metric(s), e.g., the development work effort or defect rates, for any future software project based on the project's predictor software metric(s), e.g., the project team size. Obviously, the construction of such a software metric model makes use of a data sample of such metrics from analogous past projects. However, incomplete data often appear in such data samples. Moreover, the decision on whether a particular predictor metric should be included is most likely based on an intuitive or experience-based assumption that the predictor metric has an impact on the target metric with a statistical significance. However, this assumption is usually not verifiable "retrospectively" after the model is constructed, leading to redundant predictor metric(s) and/or unnecessary predictor metric complexity. To solve all these problems, we derived a methodology consisting of the k-nearest neighbors (k-NN) imputation method, statistical hypothesis testing, and a "goodness-of-fit" criterion. This methodology was tested on software effort metric models and software quality metric models, the latter usually suffers from far more serious incomplete data. This paper documents this methodology and the tests on these two types of software metric models.

KW - Imputation method

KW - Missing data

KW - Model simplification

KW - Models

KW - Software metrics

KW - Software quality

UR - http://www.scopus.com/inward/record.url?scp=38849129507&partnerID=8YFLogxK

U2 - 10.1142/S0218194007003495

DO - 10.1142/S0218194007003495

M3 - Article

AN - SCOPUS:38849129507

SN - 0218-1940

VL - 17

SP - 689

EP - 707

JO - International Journal of Software Engineering and Knowledge Engineering

JF - International Journal of Software Engineering and Knowledge Engineering

IS - 6

ER -