Architectural Decay as Predictor of Issue- and Change-Proneness

TL;DR

This paper demonstrates that architectural decay indicators, such as architectural smells, can be used to predict future issues and changes in software systems, even leveraging data from unrelated systems when historical data is unavailable.

Contribution

It introduces models that use architectural decay data to predict issue- and change-proneness, including cross-system predictions without historical data.

Findings

Architectural decay correlates with future issues and changes.

Models can predict issue- and change-proneness with high accuracy.

Cross-system predictions are feasible without historical data.

Abstract

Architectural decay imposes real costs in terms of developer effort, system correctness, and performance. Over time, those problems are likely to be revealed as explicit implementation issues (defects, feature changes, etc.). Recent empirical studies have demonstrated that there is a significant correlation between architectural "smells" -- manifestations of architectural decay -- and implementation issues. In this paper, we take a step further in exploring this phenomenon. We analyze the available development data from 10 open-source software systems and show that information regarding current architectural decay in these systems can be used to build models that accurately predict future issue-proneness and change-proneness of the systems' implementations. As a less intuitive result, we also show that, in cases where historical data for a system is unavailable, such data from other, unrelated systems can provide reasonably accurate issue- and change-proneness prediction capabilities.