Understanding Structural Complexity Evolution: A Quantitative Analysis
Bibliographic record
Abstract
Background: An increase in structural complexity makes the source code of software projects more difficult to understand, and consequently more difficult and expensive to maintain and evolve. Knowing the factors that influence structural complexity helps developers to avoid the effects of higher levels of structural complexity on the maintainability of their projects. Aims: This paper investigates factors that might influence the evolution of structural complexity. Method: We analyzed the source code repositories of 5 free/open source software projects, with commits as experimental units. For each commit we measured the structural complexity variation it caused, the experience of the developer who made the commit, the size variation caused by the commit, and the change diffusion of the commit. Commits that increased structural complexity were analyzed separately from commits that decreased structural complexity, since they represent activities of distinct natures. Results: Change diffusion was the most influential among the factors studied, followed by size variation and developer experience, system growth was not necessarily associated with complexity increase, all the factors we studied influenced at least two projects, different projects were affected by different factors, and the factors that influenced the increase in structural complexity were usually not the same that influenced the decrease. Conclusions: All the factors explored in this study should be taken into consideration when analysing structural complexity evolution. However, they do not fully explain the structural complexity evolution in the studied projects: this suggests that qualitative studies are needed in order to better understand structural complexity evolution and identify other factors that must be included in future quantitative analysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".