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.

ARCELLI FONTANA, F., Pigazzini, I., Roveda, R., Zanoni, M. (2017). Automatic detection of instability architectural smells. In Proceedings of the 32nd International Conference on Software Maintenance and Evolution (ICSME 2016) (pp.433-437). Raleigh : Institute of Electrical and Electronics Engineers Inc. [10.1109/ICSME.2016.33].

Automatic detection of instability architectural smells

ARCELLI FONTANA, FRANCESCA
Primo
;
Pigazzini, I;ROVEDA, RICCARDO
;
ZANONI, MARCO
Ultimo
2017

Abstract

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.
slide + paper
Architectural Smell, Design Metrics, Software Architecture Evaluation
English
IEEE International Conference on Software Maintenance and Evolution (ICSME) OCT 02-10
2016
Proceedings of the 32nd International Conference on Software Maintenance and Evolution (ICSME 2016)
9781509038060
2016
2017
433
437
7816489
reserved
ARCELLI FONTANA, F., Pigazzini, I., Roveda, R., Zanoni, M. (2017). Automatic detection of instability architectural smells. In Proceedings of the 32nd International Conference on Software Maintenance and Evolution (ICSME 2016) (pp.433-437). Raleigh : Institute of Electrical and Electronics Engineers Inc. [10.1109/ICSME.2016.33].
File in questo prodotto:
File Dimensione Formato  
ASdetection.pdf

Solo gestori archivio

Descrizione: ultima versione della pubblicazione
Dimensione 591.44 kB
Formato Adobe PDF
591.44 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/130605
Citazioni
  • Scopus 68
  • ???jsp.display-item.citation.isi??? 35
Social impact