Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
10-2016
Abstract
Code metric analysis is a well-known approach for assessing the quality of a software system. However, current tools and techniques do not take the system architecture (e.g., MVC, Android) into account. This means that all classes are assessed similarly, regardless of their specific responsibilities. In this paper, we propose SATT (Software Architecture Tailored Thresholds), an approach that detects whether an architectural role is considerably different from others in the system in terms of code metrics, and provides a specific threshold for that role. We evaluated our approach on 2 different architectures (MVC and Android) in more than 400 projects. We also interviewed 6 experts in order to explain why some architectural roles are different from others. Our results shows that SATT can overcome issues that traditional approaches have, especially when some architectural role presents very different metric values than others.
Keywords
Code metric analysis, Software quality, System architecture, Software Architecture Tailored Thresholds (SATT)
Discipline
Databases and Information Systems | Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
Proceedings of the 16th International Working Conference on Source Code Analysis and Manipulation, Raleigh, USA, 2016 October 2-3
First Page
41
Last Page
50
ISBN
9781509038503
Identifier
10.1109/SCAM.2016.19
Publisher
Institute of Electrical and Electronics Engineers Inc.
City or Country
Raleigh, United States
Citation
ANICHE, Maurício; TREUDE, Christoph; ZAIDMAN, Andy; VAN DEURSEN, Arie; and GEROSA, Marco Aurélio.
SATT: Tailoring code metric thresholds for different software architectures. (2016). Proceedings of the 16th International Working Conference on Source Code Analysis and Manipulation, Raleigh, USA, 2016 October 2-3. 41-50.
Available at: https://ink.library.smu.edu.sg/sis_research/8773
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/SCAM.2016.19