Monitoring　and　predicting　the　trend　of　bug　number　time　series　of　a　software　system　is　crucial　for　both　software　project　managers　and　software　end-users.　For　software　managers,　accurate　prediction　of　bug　number　of　a　software　system　will　assist　them　in　making　timely　decisions,　such　as　effort　investment　and　resource　allocation.　For　software　end-users,　knowing　possible　bug　number　of　their　systems　ahead　will　enable　them　to　adopt　timely　actions　in　coping　with　the　loss　caused　by　possible　system　failures.　This　study　proposes　an　approach　called　SamEn-SVR　to　combine　sample　entropy　and　support　vector　regression　(SVR)　to　predict　software　bug　number　using　time　series　analysis.　The　basic　idea　is　to　use　template　vectors　with　the　smallest　complexity　as　input　vectors　for　SVR　classifiers　to　ensure　predictability　of　time　series.　By　using　Mozilla　Firefox　bug　data,　we　conduct　extensive　experiments　to　compare　the　proposed　approach　and　state-of-the-art　techniques　including　auto-regressive　integrated　moving　average　(ARIMA),　X12　enhanced　ARIMA　and　polynomial　regression　to　predict　bug　number　time　series.　Experimental　results　demonstrate　that　the　proposed　SamEn-SVR　approach　outperforms　state-of-the-art　techniques　in　bug　number　prediction.