Computation　offloading　is　a　promising　way　of　improving　the　performance　and　reducing　the　battery　power　consumption,　since　it　moves　some　time-consuming　computation　activities　to　nearby　servers.　Although　various　approaches　have　been　proposed　to　support　computation　offloading,　we　argue　that　there　is　still　sufficient　space　for　improvements,　since　existing　approaches　cannot　accurately　estimate　the　execution　costs.　As　a　result,　we　find　that　their　offloading　plans　are　less　optimized.　To　handle　the　problem,　in　this　paper,　given　an　Android　application,　we　propose　a　novel　approach,　called　ANDROIDOFF,　that　supports　offloading　at　the　granularity　of　objects.　Supporting　such　capability　is　challenging　due　to　the　two　reasons:　(1)　through　dynamic　execution,　it　is　feasible　to　collect　the　execution　costs　of　only　partial　methods,　and　(2)　it　is　difficult　to　accurately　estimate　the　execution　costs　of　the　remaining　methods.　To　overcome　the　challenges,　given　an　Android　application,　ANDROIDOFF　first　combines　static　and　dynamic　analysis　to　predict　the　execution　costs　of　all　its　methods.　After　all　the　costs　are　estimated,　ANDROIDOFF　synthesizes　an　offloading　plan,　in　which　determines　the　offloading　details.　We　evaluate　ANDROIDOFF　on　a　real-world　application,　with　two　mobile　devices.　Our　results　show　that,　compared　with　other　approaches,　ANDROIDOFF　saves　the　response　time　by　8%-49%　and　reduces　the　energy　consumption　by　12%-49%　on　average　for　computation-intensive　applications.　(C)　2019　Elsevier　Inc.　All　rights　reserved.