Android　Auto　is　designed　to　enhance　the　driving　experience　by　extending　dashboards　of　cars　with　smartphones＇　functionalities,　among　which　an　essential　one　is　the　message　flow　via　notification　mechanism.　This　paper　investigates　the　quality　of　current　compatible　apps,　and　locates　two　main　error-prone　points.　The　study　begins　with　manually　designed　black-box　testing　models　including　finite　state　machine　and　combinatorial　input　model　according　to　safety　requirements,　and　extracts　testing　suites　from　them.　The　tests　are　executed　on　17　popular　apps　and　reveal　dozens　of　defects　that　might　result　in　safety　risks　or　inferior　driving　experiences.　These　defects　are　manually　inspected　and　organized　into　several　patterns.　The　experience　and　lessons　from　this　empirical　study　are　helpful　to　the　detailed　design　and　implementation　of　messaging　modules.　©　2019　IEEE.