TY - GEN
T1 - Combining and adapting software quality predictive models by genetic algorithms
AU - Azar, D.
AU - Precup, D.
AU - Bouktif, S.
AU - Kégl, B.
AU - Sahraoui, H.
N1 - Publisher Copyright:
© 2002 IEEE.
PY - 2002
Y1 - 2002
N2 - The goal of quality models is to predict a quality factor starting from a set of direct measures. Selecting an appropriate quality model for a particular software is a difficult, non-trivial decision. In this paper, we propose an approach to combine and/or adapt existing models (experts) in such way that the combined/adapted model works well on the particular system. Test results indicate that the models perform significantly better than individual experts in the pool.
AB - The goal of quality models is to predict a quality factor starting from a set of direct measures. Selecting an appropriate quality model for a particular software is a difficult, non-trivial decision. In this paper, we propose an approach to combine and/or adapt existing models (experts) in such way that the combined/adapted model works well on the particular system. Test results indicate that the models perform significantly better than individual experts in the pool.
UR - http://www.scopus.com/inward/record.url?scp=84982920811&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84982920811&partnerID=8YFLogxK
U2 - 10.1109/ASE.2002.1115031
DO - 10.1109/ASE.2002.1115031
M3 - Conference contribution
AN - SCOPUS:84982920811
T3 - Proceedings - ASE 2002: 17th IEEE International Conference on Automated Software Engineering
SP - 285
EP - 288
BT - Proceedings - ASE 2002
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th IEEE International Conference on Automated Software Engineering, ASE 2002
Y2 - 23 September 2002 through 27 September 2002
ER -