Detecting the Locations and Predicting the Maintenance Costs of Compound Architectural Debts

Abstract

Architectural Technical Debt ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ATD</i> ) refers to sub-optimal architectural design in a software system that incurs high maintenance “interest” over time. Previous research revealed that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ATD</i> has significant negative impact on daily development. This paper contributes an approach to enable an architect to precisely locate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ATDs</i> , 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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ATDs</i> are expressed in four typical patterns, which entail the core of each debt. Furthermore, we aggregate compound <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ATDs</i> to capture the complicated relationship among multiple <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ATD</i> instances, which should be examined together for effective refactoring solutions. We evaluate our approach on 18 real-world projects. We identified <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ATDs</i> that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">persistently</i> incur 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 related <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ATDs</i> , 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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ATDs</i> in their systems.