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The past few years saw the rise of microservices studies and best practices, along with wide industrial adoption of this architectural style. We now witness the birth of another challenging topic: microservices quality. Like other kinds of architectures, also microservices suffer from erosion and technical debt, whose symptoms can be the appearance of microservices smells, which impact negatively on the system's quality, by hindering, for example, its maintainability. In this paper we propose a tool called Aroma, to reconstruct microservices architectures and detect microservices smells, based on the dynamic analysis of microservices execution traces. We describe the main features of the tool, the strategies adopted for microservice smells detection and the first preliminary experimentation.

In this paper, we share our experience in designing and running a project-based exam for a Software Engineering course at the undergraduate level. We underline the teaching objectives and content of the course which aim to prepare students for the industrial environment, e.g., various design perspectives on the project, software quality assurance and evaluation, tools for development and software quality assessment, project management and team work. We present our project-based exam and summarize hints on its design, organization, and evaluation. Project examples are also introduced.

Although architectural smells are one of the most studied type of architectural technical debt, their impact on maintenance effort has not been thoroughly investigated. Studying this impact would help to understand how much technical debt interest is being paid due to the existence of architecture smells and how this interest can be calculated. This work is a first attempt to address this issue by investigating the relation between architecture smells and source code changes. Specifically, we study whether the frequency and size of changes are correlated with the presence of a selected set of architectural smells. We detect architectural smells using the Arcan tool, which detects architectural smells by building a dependency graph of the system analyzed and then looking for the typical structures of the architectural smells. The findings, based on a case study of 31 open-source Java systems, show that 87% of the analyzed commits present more changes in artifacts with at least one smell, and the likelihood of changing increases with the number of smells. Moreover, there is also evidence to confirm that change frequency increases after the introduction of a smell and that the size of changes is also larger in smelly artifacts. These findings hold true especially in Medium–Large and Large artifacts.

Architectural anomalies, also known as architectural smells, represent the violation of design principles or decisions that impact internal software qualities with significant negative effects on maintenance, evolution costs and technical debt. Architectural smells, if early removed, have an overall impact on reducing a possible progressive architectural erosion and architectural debt. Some tools have been proposed for their detection, exploiting different methods, usually based only on static analysis. This work analyzes how dynamic analysis can be exploited to detect architectural smells. We focus on two smells, Hub-Like Dependency and Cyclic Dependency, and we extend an existing tool integrating dynamic analysis. We conduct an empirical study on ten projects. We compare the results obtained comparing a method featuring dynamic analysis and the original version of Arcan based only on static analysis to understand if dynamic analysis can be successfully used. The results show that dynamic analysis helps identify missing architectural smells instances, although its usage is hindered by the lack of test suites suitable for this scope.

Un'architettura software è erosa (o degradata) se mostra una progressiva perdita di integrità strutturale a causa di violazioni dei principi di progettazione. I sistemi erosi soffrono di Architectural Technical Debt (ATD), lo sforzo aggiuntivo richiesto agli sviluppatori per gestire i difetti causati dall'erosione. Un sintomo dell'accumulo di ATD è la presenza di Architectural Smells (AS), decisioni di software design che hanno un impatto negativo sulla qualità interna del sistema software. I sistemi affetti da AS soffrono di maggiori costi di manutenzione e sono più difficili da evolvere. Questa tesi indaga sei diversi tipi di AS che violano diversi principi di design in progetti Java monolitici Open-Source e industriali. Identifichiamo gli AS con il nostro strumento, Arcan, e introduciamo la sua nuova estensione per la rappresentazione dei software concerns. Discutiamo poi gli AS dal punto di vista dei professionisti, cercando di riassumere come gli AS sono percepiti e validando i risultati di Arcan. Riportiamo anche i risultati dei nostri studi empirici riguardanti l'evoluzione e la correlazione di AS e ATD. Infine, presentiamo i nostri primi risultati riguardanti la migrazione e la manutenzione di architetture a microservizi, con particolare attenzione al rilevamento degli odori dei microservizi., A software architecture is eroded (or degraded) if it shows a progressive loss of structural integrity due to design principle violations which leads to the deviation of the implemented architecture from the intended architecture. Eroded systems suffer from Architectural Technical Debt (ATD), the additional effort required by developers to manage the shortcomings caused by the erosion. A symptom of the accumulation of ATD is the presence of Architectural Smells (AS), design decisions that impact negatively on the internal system quality. Systems affected by AS suffer from higher maintenance costs and are harder to evolve. This thesis investigates six different types of AS violating different design principles in Open-Source and industrial monolithic Java projects. We identify AS with our tool, Arcan, and introduce its new extension for the representation of software concerns. We then discuss AS from the point of view of practitioners, trying to summarise how AS are perceived and validating Arcan results. We also report the results of our empirical studies concerning AS and ATD evolution and correlation. Finally, we present our first results concerning the migration and maintenance of microservices architectures, with a focus on the detection of microservices smells.