Automatic Detection of Instability Architectural Smells

Proceedings of the 21st International Conference on Evaluation and Assessment in Software Engineering

Zanoni

et al. 2017

Self Cite

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:

Section: Discussionmentioning

confidence: 99%

Change Prediction through Coding Rules Violations

Tollin

Proceedings of the 21st International Conference on Evaluation and Assessment in Software Engineering

Zanoni

et al. 2017

Self Cite

“…Although in a previous work we experimented Arcan on several projects [6], a more careful qualitative evaluation of Arcan results performed by external tool developers other than ourselves was never previously performed. In the following, we report on our first attempts at this kind of validation on the following two projects:…”

Section: Validation Of Arcan Resultsmentioning

confidence: 99%

“…In a previous work [6], we outlined the detection algorithms hardcoded within Arcan. The original contribution of this paper is its focus on: (a) the tool's architecture and inner workings; (b) on the improvements of its detection strategies for the Cycle Dependency smell, and (c) on the manual validation of the detection results of Arcan done by real-life software developers.…”

Section: Introductionmentioning

confidence: 99%

Arcan: A Tool for Architectural Smells Detection

2017 IEEE International Conference on Software Architecture Workshops (ICSAW)

Pigazzini

Roveda

et al. 2017

Self Cite

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.

“…We have performed four types of analyses: a generic data mining analysis to have a better understanding of the data, a trend analysis to understand the evolution of the smells over time, a correlation analysis to identify possible correlations among the smell characteristics 1 considered, and a survival analysis to document their probability to persist within the system. The focus of this study is on the architectural smells known as instability AS [6]; these are introduced in more depth in Section III.…”

Section: Introductionmentioning

confidence: 99%

Investigating Instability Architectural Smells Evolution: An Exploratory Case Study

Sas

Avgeriou

2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)

2019

Architectural smells may substantially increase maintenance effort and thus require extra attention for potential refactoring. While we currently understand this concept and have identified different types of such smells, we have not yet studied their evolution in depth. This is necessary to inform their prioritisation and refactoring. This study analyses the evolution of individual architectural smell instances over time, and the characteristics that define these instances. Three different types of architectural smells are taken into consideration and mined from a total of 524 versions across 14 different projects. The results show how different smell types differ in multiple aspects, such as their growth rate, the importance of the affected elements over time in the dependency network of the system, and the time each instance affects the system. They also cast valuable insights on what aspects are the most important to consider during prioritisation and refactoring activities.