Refactoring code smells in a project can be easily done, while refactoring architectural smells is a more complex task that could have different effects on the overall quality of a project. Architectural smells represent one of the greatest sources of technical debt faced by practitioners and are symptoms of architectural degradation. In this paper, we investigate the impact of the refactoring of architectural smells on technical debt and other software quality metrics. We focus our attention on smells found in microservice projects and also explore whether the refactoring of these smells can support the identification of new microservices. We analyze microservice projects due to the increasing interest in this kind of software architecture from academia and practitioners. The results obtained outline how architectural smells refactoring has to be carefully taken into account in order to improve software quality and reduce technical debt.

Context: Architectural smells, are a well-known indicator of architectural technical debt, their presence could have a great impact on the maintainability and evolvability of a project. Hence, it is important to carefully study and monitor them. Objective: In this paper, we describe an empirical study on the analysis of the correlations existing between architectural smells and co-changes, with the aim of getting further insights into how architectural smells can influence maintenance efforts. Method: Using the Goal-Question-Metric approach, we compared pairs of files affected by smells with clean ones to determine if smelly pairs co-change more frequently. To collect the data, we exploit a new data collection pipeline based on Apache Airflow to generate large-scale, up-to-date datasets with static analysis tools. For the current study, the pipeline uses Arcan 2 , a static analysis tool for architectural smell detection. Results: The empirical study, conducted on a set of projects analyzed by the pipeline, found that the median Co-change rate in smelly (both files affected) and mixed (one file affected) pairs was higher than in clean pairs. Moreover, the Co-change rate of the smelly pairs is higher than that of the mixed ones. This result became more significant as the lines of code increased. Conclusion: The empirical study found that architectural smells are linked to higher Co-change rates in affected files, leading to increased maintenance efforts for developers. Moreover, the results highlight the value of the pipeline data and offer useful insights for managing architectural technical debt.