Conference proceeding
Understanding Architectural Complexity, Maintenance Burden, and Developer Sentiment -A Large-Scale Study
Proceedings / International Conference on Software Engineering, pp 2176-2187
26 Apr 2025
Abstract
Intuitively, the more complex a software system is, the harder it is to maintain. Statistically, it is not clear which complexity metrics 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 metrics, it remains difficult to objectively monitor maintenance, control complexity, or justify refactoring. In this paper, we report a largescale study of 1252 projects written in C++ and Java from Google LLC. We collected three categories of metrics: (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 analyzed the correlations among these metrics 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 intuition 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.
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Details
- Title
- Understanding Architectural Complexity, Maintenance Burden, and Developer Sentiment -A Large-Scale Study
- Creators
- Yuanfang Cai - Drexel UniversityLanting He - GoogleJun Qian - GoogleYony Kochinski - GoogleNan Zhang - GoogleCiera Jaspan - GoogleAntonio Bianco - Google LLC,USA
- Publication Details
- Proceedings / International Conference on Software Engineering, pp 2176-2187
- Series
- International Conference on Software Engineering
- Publisher
- IEEE; LOS ALAMITOS
- Number of pages
- 12
- Grant note
- National Science Foundation of the US: CCF-2232720, CCF-2213764, TI-2236824
This work was supported in part by the National Science Foundation of the US under grants CCF-2232720, CCF-2213764, and TI-2236824.
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science (Computing)
- Web of Science ID
- WOS:001538318100170
- Scopus ID
- 2-s2.0-105010330926
- Other Identifier
- 991022061654104721