Understanding Architectural Complexity, Maintenance Burden, and Developer Sentiment -- A Large-Scale Study

Intuitively, the more complex a software system is, the harder it is to maintain. Statistically, it is not clear which complexity measures correlate with maintenance effort; in fact, it is not even clear how to objectively measure maintenance burden, so that developers’ sentiment and intuition can be supported by numbers. Without effective complexity and maintenance measures, it remains difficult to objectively monitor maintenance, control complexity, or justify refactoring. In this paper, we report a large-scale study of 1200+ projects written in C++ and Java from Google LLC. In this study, we collected three categories of measures: (1) architectural complexity, measured using propagation cost (PC), decoupling level (DL), and structural anti-patterns; (2) maintenance activity, measured using the number of changes, lines of code (LOC) written, and active coding time (ACT) spent on feature-addition vs. bug-fixing, and (3) developer sentiment on complexity and productivity, collected from 7200 survey responses. We statistically analysed the correlations among these measures and obtained significant evidence of the following findings: 1) the more complex the architecture is (higher propagation cost, more instances of anti-patterns), the more LOC is spent on bug-fixing, rather than adding new features; 2) developers who commit more changes for features, spend more lines of code on features, or spend more time on features also feel that they are less hindered by technical debt and complexity. To the best of our knowledge, this is the first large-scale empirical study establishing the statistical correlation among architectural complexity, maintenance activity, and developer sentiment. The implication is that, instead of solely relying upon developer sentiment and intuitions to detect degraded structure or increased burden to evolve, it is possible to objectively and continuously measure and monitor architectural complexity and maintenance difficulty, increasing feature delivery efficiency by reducing architectural complexity and anti-patterns.