Automated Code-Smell Detection in Microservices Through Static Analysis: A Case Study

Abstract

Microservice Architecture (MSA) is becoming the predominant direction of new cloud-based applications. There are many advantages to using microservices, but also downsides to using a more complex architecture than a typical monolithic enterprise application. Beyond the normal poor coding practices and code smells of a typical application, microservice-specific code smells are difficult to discover within a distributed application setup. There are many static code analysis tools for monolithic applications, but tools to offer code-smell detection for microservice-based applications are lacking. This paper proposes a new approach to detect code smells in distributed applications based on microservices. We develop an MSANose tool to detect up to eleven different microservice specific code smells and share it as open-source. We demonstrate our tool through a case study on two robust benchmark microservice applications and verify its accuracy. Our results show that it is possible to detect code smells within microservice applications using bytecode and/or source code analysis throughout the development process or even before its deployment to production.

On Microservice Analysis and Architecture Evolution: A Systematic Mapping Study

“…The authors validated their works on five open-source projects and further discussed the roadmap for implementing the next set of smells. A similar study was conducted by Walker et al [27,28] where the authors identified 11 microservice-specific code smells using static analysis. They also used several adjustable threshold values to change the flexibility of pattern matching.…”

“…Most papers that used static analysis as their main method tended to do so to gather information specifically about the architecture. These techniques depend on analyzing statically defined artifacts to reconstruct an architectural view of a system, mainly analyzing source code [16,26,28,30], but also other artifacts, such as OpenAPI specifications [15]. Static analysis is applicable to other goals as well, such as anti-pattern or code smell detection [26,28,29].…”

“…Then it aggregates them into a full-scope centralized perspective for each view, which consists of all the microservices aggregated into a mesh. The same principle was later extended to detect code smells searching for patterns [27,28]. Kamimura et al used source code [17] to extract microservice candidates from monolith applications.…”

“…A tracing and analytics tool called JCallGraph developed by Liu et al [53] uses graph-based analysis as a tool for developers who need to monitor and debug their systems, which works by tracing the invocations of microservices by other microservices in a system and assembling an invocation graph representation of the system. Walker et al [27,28] achieve their goal of code smell detection by performing graph-based static analysis. First, a graph is generated that shows the different interactions between the microservices by exploring each microservice for a connection to other microservices.…”

“…For example, microservice antipatterns threaten to invalidate the benefits gained using microservice in the first place, and Tighilt et al [31] analyze existing literature to identify a catalog of 16 microservice antipatterns, the definitions of which can be used to combat poor microservice design. Walker et al [27,28], and Pigazzini et al [29] take this further and propose methods of automatically detecting selected microservice antipatterns or code smells using staticcode analysis. Works identifying technical debt are also closely related to the concept of antipatterns, and the surveys performed by Bogner et al [65] and Toledo et al [63] and the solutions they proposed show promise for preventing technical debt from crippling microservice evolution.…”