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.

Microservice architecture has become the leading design for cloud-native systems. The highly decentralized approach to software development consists of relatively independent services, which provides benefits such as faster deployment cycles, better scalability, and good separation of concerns among services. With this new architecture, one can naturally expect a broad range of advancements and simplifications over legacy systems. However, microservice system design remains challenging, as it is still difficult for engineers to understand the system module boundaries. Thus, understanding and explaining the microservice systems might not be as easy as initially thought. This study aims to classify recently published approaches and techniques to analyze microservice systems. It also looks at the evolutionary perspective of such systems and their analysis. Furthermore, the identified approaches target various challenges and goals, which this study analyzed. Thus, it provides the reader with a roadmap to the discipline, tools, techniques, and open challenges for future work. It provides a guide towards choices when aiming for analyzing cloud-native systems. The results indicate five analytical approaches commonly used in the literature, possibly in combination, towards problems classified into seven categories.

Code analysis brings excellent benefits to software development, maintenance, and quality assurance. Various tools can uncover code defects or even software bugs in a range of seconds. For many projects and developers, the code analysis tools became essential in their daily routines. However, how can code analysis help in an enterprise environment? Enterprise software solutions grow in scale and complexity. These solutions no longer involve only plain objects and basic language constructs but operate with various components and mechanisms simplifying the development of such systems. Enterprise software vendors have adopted various development and design standards; however, there is a gap between what constructs the enterprise frameworks use and what current code analysis tools recognize. This manuscript aims to challenge the mainstream research directions of code analysis and motivate for a transition towards code analysis of enterprise systems with interesting problems and opportunities. In particular, this manuscript addresses selected enterprise problems apparent for monolithic and distributed enterprise solutions. It also considers challenges related to the recent architectural push towards a microservice architecture. Along with open-source proof-of-concept prototypes to some of the challenges, this manuscript elaborates on code analysis directions and their categorization. Furthermore, it suggests one possible perspective of the problem area using aspect-oriented programming.

No abstract

No abstract