Code smells and architectural smells (also called bad smells) are symptoms of poor design that can hinder code understandability and decrease maintainability. Several bad smells have been defined in the literature for both generic architectures and specific architectures. However, cloud-native applications based on microservices can be affected by other types of issues. In order to identify a set of microservice-specific bad smells, researchers collected evidence of bad practices by interviewing 72 developers with experience in developing systems based on microservices. Then, they classified the bad practices into a catalog of 11 microservice-specific bad smells frequently considered harmful by practitioners. The results can be used by practitioners and researchers as a guideline to avoid experiencing the same difficult situations in the systems they develop.

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On the Definition of Microservice Bad Smells

Code smells are sub-optimal coding circumstances such as blob classes or spaghetti code - they have received much attention and tooling in recent software engineering research. Higher-up in the abstraction level, architectural smells are problems or sub-optimal architectural patterns or other design-level characteristics. These have received significantly less attention even though they are usually considered more critical than code smells, and harder to detect, remove, and refactor. This paper describes an open-source tool called Arcan developed for the detection of architectural smells through an evaluation of several different architecture dependency issues. The detection techniques inside Arcan exploit graph database technology, allowing for high scalability in smells detection and better management of large amounts of dependencies of multiple kinds. In the scope of this paper, we focus on the evaluation of Arcan results carried out with real-life software developers to check if the architectural smells detected by Arcan are really perceived as problems and to get an overall usefulness evaluation of the tool.

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Arcan: A Tool for Architectural Smells Detection

Architecture is the set of principal design decisions about a software system. In practice, new architectural decisions are added and existing ones reversed or modified throughout a system’s lifetime. Frequently, these decisions deviate from the architect's well-considered intent, and software systems regularly exhibit increased architectural decay as they evolve. The manifestations of such ill-considered design decisions are seen as “architectural smells”. To date, there has been no in-depth study of the characteristics or trends involving this phenomenon. Instead, when referring to architectural smells and their negative effects, both researchers and practitioners had to rely on folklore and their personal, inherently limited experience. In this paper, we report on the systematic step we have taken in investigating the nature and impact of architectural smells. We have selected a set of representative architectural smells from literature and analyzed their instances in 421 versions from 8 open-source software systems. We have (1) developed algorithms to automatically detect instances of multiple architectural smell types, and (2) analyzed relationships between the detected smells and the lists of issues reported in the systems’ respective issue trackers. Our study shows that architectural smells have tangible negative consequences in the form of implementation issues as well as code commits requiring increased maintenance effort throughout a system’s lifetime.

An Empirical Study of Architectural Decay in Open-Source Software

Architectural smells can negatively impact on different software qualities and can represent a relevant source of architectural debt. Several architectural smells have been defined by different researchers. Moreover, both academia and industry proposed several tools for software quality analysis, but it is not always clear to understand which tools provide also support for architectural smells detection and if the tools developed for this specific purpose are effectively available or not. In this paper we propose a catalogue of architectural smells for which, at least one tool able to detect the smell exists. We outline the main differences in the detection techniques exploited by the tools and we propose a classification of these architectural smells according to the violation of three design principles.

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Architectural smells detected by tools: a catalogue proposal

With the adoption of microservices architectural styles, practitioners started noticing increasing pitfalls in managing and maintaining such architectures, with the risk of introducing architectural debt. Previous studies identified different microservice smells (also named anti-patterns) that harm microservices architectures. However, according to our knowledge, there are no tools that can automatically detect microservice smells, so their identification is left to the experience of the developer. In this paper, we extend an existing tool developed for the detection of architectural smells to explore microservices architecture through the detection of three microservice smells: Cyclic Dependencies, Hard-Coded Endpoints, and Shared Persistence. We detected the smells on five open-source projects implemented with microservices and manually validated the precision of the detection results. This work aims to open new perspectives on facing and studying architectural debt in the field of microservices architectures.

Towards microservice smells detection

Code smells represent well known symptoms of problems at code level, and architectural smells can be seen as their counterpart at architecture level. If identified in a system, they are usually considered more critical than code smells, for their effect on maintainability issues. In this paper, we introduce a tool for the detection of architectural smells that could have an impact on the stability of a system. The detection techniques are based on the analysis of dependency graphs extracted from compiled Java projects and stored in a graph database. The results combine the information gathered from dependency and instability metrics to identify flaws hidden in the software architecture. We also propose some filters trying to avoid possible false positives.

Automatic Detection of Instability Architectural Smells

Different tools adopt different terms, metrics, and ways to identify and measure technical debt. We attempt to clarify the situation by comparing the features and popularity of technical debt measurement tools and analyzing the existing empirical evidence on their validity.

An Overview and Comparison of Technical Debt Measurement Tools

Different indexes have been proposed to evaluate software quality and technical debt. Usually these indexes take into account different code level issues and several metrics, well known software metrics or new ones defined ad hoc for a specific purpose. In this paper we propose and define a new index, more oriented to the evaluation of architectural violations. We describe in detail the index, called Architectural Debt Index, that we integrated in a tool developed for architectural smell detection. The index is based on the detection of architectural smells, their criticality and their history. Currently only dependency architectural smells have been considered, but other architectural debt indicators can be considered and integrated in the index computation.

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Ilaria Pigazzini

Papers

Arcan: A Tool for Architectural Smells Detection

Automatic Detection of Instability Architectural Smells

An Overview and Comparison of Technical Debt Measurement Tools

Towards microservice smells detection

Towards an Architectural Debt Index