In　the　current　stage,　software　defect　prediction　is　suffering　the　imbalanced　data　problem.　Traditional　methods　are　insensitive　to　defect-prone　modules　and　tend　to　predict　defect-prone　modules　as　defect-free　modules.　To　deal　with　this　problem,　sampling　techniques　are　adopted　to　rebalance　the　defect-prone　and　defect-free　data　to　train　the　predictive　model　in　order　to　improve　the　performance.　However,　it　is　not　clear　on　the　combined　effect　of　the　sampling　techniques　and　the　machine　learning　classifiers　on　the　performance　of　software　defect　prediction.　The　intent　of　the　paper　is　to　study　the　performance　impact　on　defect　prediction　incurred　by　different　combinations　of　sampling　techniques　and　machine　learning　classifiers.　Specifically,　we　investigate　three　types　of　sampling　techniques　as　resampling,　spread　subsampling　and　SMOTE　(Synthetic　Minority　Over-sampling　Technique),　and　five　types　of　machine　learning　classifiers　as　C4.5,　naive　Bayes,　logistic　regression,　support　vector　machine　and　deep　learning　to　study　their　combined　effect　on　defect　prediction.　By　using　the　Friedman　test　and　Nemenyi　test,　we　find　that　there　isn＇t　an　optimal　method　among　all　the　12　combinations　in　defect　prediction.　However,　support　vector　machine　and　deep　learning　have　produced　the　best　performance　stably　among　all　the　investigated　projects.　With　ANOVA　analysis,　we　find　that　the　sampling　techniques　have　great　impact　on　the　outcomes　of　defect　prediction　because　they　produce　different　data　distributions　for　model　training.　Nevertheless,　the　sampling　proportion　has　significant　impacts　on　TPR　(True　Positive　Ratio)　and　FPR　(False　Positive　Ratio)　while　it　can　merely　influence　the　AUC　(Area　under　Curve)　and　Balance　of　logistic　regression.　We　explain　the　experimental　results　in　the　paper.　©　2022　The　Authors.　Published　by　Elsevier　B.V.