Logo image
Back
Detecting the Locations and Predicting the Costs of Compound Architectural Debts
Journal article   Open access

Detecting the Locations and Predicting the Costs of Compound Architectural Debts

Lu Xiao, Yuanfang Cai, Rick Kazman, Ran Mo and Qiong Feng
IEEE transactions on software engineering, pp 1-1
04 Aug 2021
DOI: https://doi.org/10.1109/TSE.2021.3102221
url
https://doi.org/10.1109/tse.2021.3102221View
Accepted (AM)Open Access (Publisher-Specific),  Open

Abstract

Aggregates Compounds Computer architecture debt quantification and prioritization History Maintenance engineering Software software architecture software maintenance technical debt Trajectory
Architectural Technical Debt (ATD) refers to sub-optimal architectural design in a software system that incurs high maintenance ``interest" over time. Previous research revealed that ATD has significant negative impact on daily development. This paper contributes an approach to enable an architect to precisely locate ATDs, as well as capture the trajectory of maintenance cost on each debt, based on which, predict the cost of the debt in a future release. The ATDs are expressed in four typical patterns, which entail the core of each debt. Furthermore, we aggregate compound ATDs to capture the complicated relationship among multiple ATD instances, which should be examined together for effective refactoring solutions. We evaluate our approach on 18 real-world projects. We identified ATDs that persistently incur significant (up to 95% of) maintenance costs in most projects. The maintenance costs on the majority of debts fit into a linear regression model---indicating stable ``interest" rate. In five projects, 12.1% to 27.6% of debts fit into an exponential model, indicating increasing ``interest" rate, which deserve higher priority from architects. The regression models can accurately predict the costs of the majority of (82% to 100%) debts in the next release of a system. By aggregating related ATDs, architects can focus on a small number of cost-effective compound debts, which contain a relatively small number of source files, but account for a large portion of maintenance costs in their projects. With these capabilities, our approach can help architects make informed decisions regarding whether, where, and how to refactor for eliminating ATDs in their systems.

Metrics

13 Record Views
21 citations in Web of Science
17 citations in Scopus

Details

InCites Highlights

Data related to this publication, from InCites Benchmarking & Analytics tool:

Collaboration types
Domestic collaboration
International collaboration
Web of Science research areas
Computer Science, Software Engineering
Engineering, Electrical & Electronic
Logo image