Detecting, Tracing, and Monitoring Architectural Tactics in Code
Abstract
Software architectures are often constructed through a series of design decisions. In particular, architectural tactics are selected to satisfy specific quality concerns such as reliability, performance, and security. However, the knowledge of these tactical decisions is often lost, resulting in a gradual degradation of architectural quality as developers modify the code without fully understanding the underlying architectural decisions. In this paper we present a machine learning approach for discovering and visualizing architectural tactics in code, mapping these code segments to tactic traceability patterns, and monitoring sensitive areas of the code for modification events in order to provide users with up-to-date information about underlying architectural concerns. Our approach utilizes a customized classifier which is trained using code extracted from fifty performance-centric and safety-critical open source software systems. Its performance is compared against seven off-the-shelf classifiers. In a controlled experiment all classifiers performed well; however our tactic detector outperformed the other classifiers when used within the larger context of the Hadoop Distributed File System. We further demonstrate the viability of our approach for using the automatically detected tactics to generate viable and informative messages in a simulation of maintenance events mined from Hadoop's change management system.
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Architecture monitoring aims to quantify health status of software systems continuously and evaluate whether symptoms of architecture problems crept into a system.
“Architecture monitoring aims to quantify the health status of software systems in a continuous way, and evaluate whether the symptoms of architecture problems crept into a system [65].”
From Software Architecture in Practice: Challenges and Opportunities (2023) · cites this paper as [65]
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“Architecture monitoring aims to quantify the health status of software systems in a continuous way, and evaluate whether the symptoms of architecture problems crept into a system [63].”
Background
- Authors
- Mirakhorli, M., & Cleland-Huang, J.
- Affiliations
- Rochester Institute of Technology (US)
- ORCID
- Mirakhorli, Mehdi
- Venue
- IEEE Transactions on Software Engineering
- Published
- 2016-03-01
- Volume
- 42
- Issue
- 3
- Pages
- 205-220
- ISSN
- 0098-5589 (ISSN-L)
- 1939-3520
- OpenAlex
- W2296164881
- Cited by
- 85
- References
- 64
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 4 · 2023: 11 · 2022: 8 · 2021: 12 · 2020: 9 · 2019: 8 · 2018: 12 · 2017: 8 · 2016: 6 · 2015: 1
- FWCI
- 24.27
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Software Engineering (publishedVersion)
- Funders
- US National Science Foundation (CCF-0810924)
- Research Experience for Undergraduates (CCF 1341072)
- Topics
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Software Engineering Methodologies
- Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
- Keywords
- architectural tactics, software architecture, code classification, architectural quality, software maintenance, change management, HDFS, safety-critical software
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