TY - GEN

T1 - Optimizing and simplifying software metric models constructed using maximum likelihood methods

AU - Chan, Victor K.Y.

AU - Wong, W. Eric

PY - 2005

Y1 - 2005

N2 - A software metric model can be used to predict a target metric (e.g., the development work effort) for a future release of a software system based on the project's predictor metrics (e.g., the project team size). However, missing or incomplete data often appear in the data samples used to construct the model. So far, the least biased and thus the most recommended software metric models for dealing with the missing/incomplete data are those constructed by using the maximum likelihood methods. It is true that the inclusion of a particular predictor metric in the model construction is initially based on an intuitive or experience-based assumption that the predictor metric impacts significantly the target metric. Nevertheless, this assumption has to be verified. Previous research on metric models constructed by using the maximum likelihood methods simply took this verification for granted. This can result in probable inclusion of superfluous predictor metric(s) and/or unnecessary predictor metric complexity. In this paper, we propose a methodology to optimize and simplify such models based on the results of appropriate hypothesis tests. An experiment is also reported to demonstrate the use of our methodology in trimming redundant predictor metric(s) and/or unnecessary predictor metric complexity.

AB - A software metric model can be used to predict a target metric (e.g., the development work effort) for a future release of a software system based on the project's predictor metrics (e.g., the project team size). However, missing or incomplete data often appear in the data samples used to construct the model. So far, the least biased and thus the most recommended software metric models for dealing with the missing/incomplete data are those constructed by using the maximum likelihood methods. It is true that the inclusion of a particular predictor metric in the model construction is initially based on an intuitive or experience-based assumption that the predictor metric impacts significantly the target metric. Nevertheless, this assumption has to be verified. Previous research on metric models constructed by using the maximum likelihood methods simply took this verification for granted. This can result in probable inclusion of superfluous predictor metric(s) and/or unnecessary predictor metric complexity. In this paper, we propose a methodology to optimize and simplify such models based on the results of appropriate hypothesis tests. An experiment is also reported to demonstrate the use of our methodology in trimming redundant predictor metric(s) and/or unnecessary predictor metric complexity.

KW - Maximum likelihood method

KW - Modeling

KW - Software metrics

UR - http://www.scopus.com/inward/record.url?scp=33751058682&partnerID=8YFLogxK

U2 - 10.1109/COMPSAC.2005.116

DO - 10.1109/COMPSAC.2005.116

M3 - Conference contribution

AN - SCOPUS:33751058682

SN - 0769522092

SN - 9780769522098

T3 - Proceedings - International Computer Software and Applications Conference

SP - 65

EP - 70

BT - Proceedings of the 29th Annual International Computer Software and Applications Conference - Workshops and Fast Abstracts, COMPSAC 2005

T2 - 29th Annual International Computer Software and Applications Conference, COMPSAC 2005

Y2 - 26 July 2005 through 28 July 2005

ER -