Conference proceeding
Identifying and Quantifying Architectural Debt
2016 IEEE/ACM 38TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE), pp 488-498
01 Jan 2016
Abstract
Our prior work showed that the majority of error-prone source files in a software system are architecturally connected. Flawed architectural relations propagate defects among these files and accumulate high maintenance costs over time, just like debts accumulate interest. We model groups of architecturally connected files that accumulate high maintenance costs as architectural debts. To quantify such debts, we formally de fine architectural debt, and show how to automatically identify debts, quantify their maintenance costs, and model these costs over time. We describe a novel hi story coupling probability matrix for this purpose, and identify architecture debts using 4 patterns of architectural flaws shown to correlate with reduced software quality. We evaluate our approach on 7 large-scale open source projects, and show that a significant portion of total project maintenance effort is consumed by paying interest on architectural debts. The top 5 architectural debts, covering a small portion (8% to 25%) of each project's error-prone files, capture a significant portion (20% to 61%) of each project's maintenance effort. Finally, we show that our approach reveals how architectural issues evolve into debts over time.
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Details
- Title
- Identifying and Quantifying Architectural Debt
- Creators
- Lu Xiao - Drexel UniversityYuanfang Cai - Drexel UniversityRick Kazman - University of Hawaii SystemRan Mo - Drexel UniversityQiong Feng - Drexel UniversityIEEE
- Publication Details
- 2016 IEEE/ACM 38TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE), pp 488-498
- Series
- International Conference on Software Engineering
- Publisher
- IEEE
- Number of pages
- 11
- Grant note
- FA8721-05-C-0003 / Department of Defense; United States Department of Defense CCF-1065189; CCF-1514315; CCF-1514561 / National Science Foundation; National Science Foundation (NSF) Carnegie Mellon University 1514315 / Direct For Computer & Info Scie & Enginr; National Science Foundation (NSF); NSF - Directorate for Computer & Information Science & Engineering (CISE)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:000406138600044
- Scopus ID
- 2-s2.0-84971483289
- Other Identifier
- 991019167468704721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Software Engineering
- Engineering, Electrical & Electronic