Detecting the Locations and Predicting the Maintenance Costs of Compound Architectural Debts
Lu Xiao, Yuanfang Cai, Rick Kazman, Ran Mo, Qiong Feng · IEEE Transactions on Software Engineering · 2021
Architectural Technical Debt (ATD) refers to sub-optimal architectural design in a software system that incurs high maintenance “interest” over time. Previous research revealed thatATDhas significant negative impact on daily development. This paper contributes an approach to enable an architect to precisely locateATDs, 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. TheATDsare expressed in four typical patterns, which entail the core of each debt. Furthermore, we aggregate compoundATDsto capture the complicated relationship among multipleATDinstances, which should be examined together for effective refactoring solutions. We evaluate our approach on 18 real-world projects. We identifiedATDsthatpersistentlyincur significant (up to 95 percent 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 percent 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 percent) debts in the next release of a system. By aggregating relatedATDs, 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 eliminatingATDsin their systems.