Abstract-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 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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Architectural technical debt can have a huge impact on software maintainability and evolution. Hence, di↵erent architectural violations, detected as architectural smells, need to be identified and refactored. In this paper, we conducted a multiple case-study on several architectural smells detected in four industrial projects. We conducted an in-depth investigation with a questionnaire, interviews and thorough inspection of the code with the practitioners. We evaluated the negative impact of the technical debt detected by the architectural smells, their di culty to be refactored and the usefulness of the detection tool. The results show that practitioners appreciated the help of automatic detection, and that they prioritize refactoring architectural debt that causes more negative impact despite the higher refactoring e↵ort.

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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Static source code analysis is an increasingly important activity to manage software project quality, and is often found as a part of the development process. A widely adopted way of checking code quality is through the detection of violations to speci c sets of rules addressing good programming practices. SonarQube is a platform able to detect these violations, called Issues. In this paper we described an empirical study performend on two industrial projects, where we used Issues extracted on di erent versions of the projects to predict changes in code through a set of machine learning models. We achieved good detection performances, especially when predicting changes in the next version. This result paves the way for future investigations of the interest in an industrial setting towards the prioritization of Issues management according to their impact on change-proneness. CCS CONCEPTS• Software engineering → Software evolution.; Software maintenance tools.; KEYWORDS software quality, change prediction, issues, machine learning. ACM Reference format: