TY - GEN
T1 - Different strokes for different folks
T2 - 2nd International Workshop on Emerging Trends in Software Metrics, WETSoM 2011, Co-located with 33rd ACM/IEEE International Conference on Software Engineering, ICSE 2011
AU - Misirli, Ayse Tosun
AU - Caglayan, Bora
AU - Miranskyy, Andriy V.
AU - Bener, Ayse
AU - Ruffolo, Nuzio
PY - 2011
Y1 - 2011
N2 - Defect prediction has been evolved with variety of metric sets, and defect types. Researchers found code, churn, and network metrics as significant indicators of defects. However, all metric sets may not be informative for all defect categories such that only one metric type may represent majority of a defect category. Our previous study showed that defect category sensitive prediction models are more successful than general models, since each category has different characteristics in terms of metrics. We extend our previous work, and propose specialized prediction models using churn, code, and network metrics with respect to three defect categories. Results show that churn metrics are the best for predicting all defects. The strength of correlation for code and network metrics varies with defect category: Network metrics have higher correlations than code metrics for defects reported during functional testing and in the field, and vice versa for defects reported during system testing.
AB - Defect prediction has been evolved with variety of metric sets, and defect types. Researchers found code, churn, and network metrics as significant indicators of defects. However, all metric sets may not be informative for all defect categories such that only one metric type may represent majority of a defect category. Our previous study showed that defect category sensitive prediction models are more successful than general models, since each category has different characteristics in terms of metrics. We extend our previous work, and propose specialized prediction models using churn, code, and network metrics with respect to three defect categories. Results show that churn metrics are the best for predicting all defects. The strength of correlation for code and network metrics varies with defect category: Network metrics have higher correlations than code metrics for defects reported during functional testing and in the field, and vice versa for defects reported during system testing.
KW - churn metrics
KW - network metrics
KW - software defect prediction
KW - static code metrics
UR - http://www.scopus.com/inward/record.url?scp=79959856372&partnerID=8YFLogxK
U2 - 10.1145/1985374.1985386
DO - 10.1145/1985374.1985386
M3 - Conference contribution
AN - SCOPUS:79959856372
SN - 9781450305938
T3 - Proceedings - International Conference on Software Engineering
SP - 45
EP - 51
BT - WETSoM'11 - Proceedings of the 2nd International Workshop on Emerging Trends in Software Metrics, Co-located with ICSE 2011
Y2 - 24 May 2011 through 24 May 2011
ER -
