The　rapid　technological　revolution　of　Industry　4.0　has　set　off　rapid　advancement　in　the　smart　product　development.　Testing　the　smart　product＇s　system　before　release　to　market　is　crucial　for　ensuring　proper　functioning.　Smart　product　manufacturers　must　carefully　consider　different　testing　modes　to　choose　the　optimal　one　based　on　product　characteristics　and　consumer＇s　shopping　intention.　Additionally,　different　testing　modes　have　varying　impacts　on　the　environment　and　social　welfare,　as　well　as　profitability　of　the　product.　This　study　utilizes　a　two-period　game　theory　model　to　analyze　a　smart　product　manufacturer＇s　optimal　testing　mode　and　pricing　decision.　Three　testing　mode　are　evaluated:　manual　testing　mode　(MT　mode),　artificial　intelligence　testing　mode　(AT　mode),　and　public　testing　mode　(PT　mode).　Consumers＇　utility　is　influenced　by　factors　such　as　consumer　preferences,　product　quality,　usage　duration,　and　network　externality　effect.　Our　findings　indicate　that　the　optimal　testing　mode　depends　on　quality　improvement　and　network　effect　coefficients.　Specifically,　when　the　degree　of　quality　improvement　is　high　and　the　network　effect　is　moderate,　MT　mode　is　preferred;　when　the　quality　improvement　is　high　but　the　network　effect　is　small,　AT　mode　dominates;　under　other　conditions,　PT　mode　is　optimal.　Quality　improvement　and　network　effect　coefficients　have　similar　effects　on　pricing　decisions　but　impact　the　profits　in　different　manners.　Social　welfare　is　the　highest　under　MT　mode　when　the　network　effect　is　high　and　quality　improvement　is　low,　under　AT　mode　when　network　effect　is　low　and　quality　improvement　is　high,　and　under　PT　mode　when　both　factors　are　high　or　low.　Finally　we　provide　a　numerical　example　of　how　profit-driven　business　decisions　may　conflict　with　social　welfare,　potentially　leading　to　lower　social　welfare　than　the　theoretical　optimum　under　the　chosen　testing　mode　strategy.