%AWalker, A.%ADas, D.%ACerny, T.%Anull Ed.%Anull Ed.%Anull Ed.%D2021%I %K %MOSTI ID: 10310336 %PMedium: X %TAutomated Microservice Code-Smell Detection %XMicroservice Architecture (MSA) is rapidly taking over modern software engineering and becoming the predominant architecture of new cloud-based applications (apps). There are many advantages to using MSA, but there are many downsides to using a more complex architecture than a typical monolithic enterprise app. Beyond the normal bad coding practices and code-smells of a typical app, MSA specific code-smells are difficult to discover within a distributed app. There are many static code analysis tools for monolithic apps, but no tool exists to offer code-smell detection for MSA-based apps. This paper proposes a new approach to detect code smells in distributed apps based on MSA. We develop an open-source tool, MSANose, which can accurately detect up to eleven different types of MSA specific code smells. We demonstrate our tool through a case study on a benchmark MSA app and verify its accuracy. Our results show that it is possible to detect code-smells within MSA apps using bytecode and or source code analysis throughout the development or before deployment to production. Country unknown/Code not availablehttps://doi.org/10.1007/978-981-33-6385-4_20OSTI-MSA