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 metrics correlate with maintenance effort; in fact, it is not even clear how to objectively measure maintenance burden, so that developers' sentiment and intuition c...
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| Vydané v: | Proceedings / International Conference on Software Engineering s. 2176 - 2187 |
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| Hlavní autori: | , , , , , , |
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IEEE
26.04.2025
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| ISSN: | 1558-1225 |
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| 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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| AbstractList | 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. |
| Author | Jaspan, Ciera Qian, Jun Cai, Yuanfang Zhang, Nan Kochinski, Yony He, Lanting Bianco, Antonio |
| Author_xml | – sequence: 1 givenname: Yuanfang surname: Cai fullname: Cai, Yuanfang email: yuanfang.cai@drexel.edu organization: Drexel University,USA – sequence: 2 givenname: Lanting surname: He fullname: He, Lanting email: lantinghe@google.com organization: Google LLC,USA – sequence: 3 givenname: Jun surname: Qian fullname: Qian, Jun email: juqian@google.com organization: Google LLC,USA – sequence: 4 givenname: Yony surname: Kochinski fullname: Kochinski, Yony email: yonyk@google.com organization: Google LLC,USA – sequence: 5 givenname: Nan surname: Zhang fullname: Zhang, Nan email: nanzh@google.com organization: Google LLC,USA – sequence: 6 givenname: Ciera surname: Jaspan fullname: Jaspan, Ciera email: ciera@google.com organization: Google LLC,USA – sequence: 7 givenname: Antonio surname: Bianco fullname: Bianco, Antonio email: abianco@google.com organization: Google LLC,USA |
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| Snippet | Intuitively, the more complex a software system is, the harder it is to maintain. Statistically, it is not clear which complexity metrics correlate with... |
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| SubjectTerms | Complexity theory Correlation Costs Internet Java Maintenance Monitoring Software Design Software Maintenance Software measurement Software Metrics Surveys Time measurement |
| Title | Understanding Architectural Complexity, Maintenance Burden, and Developer Sentiment -A Large-Scale Study |
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