Causes of premature aging during software development
Bibliographic record
Abstract
Much work has been done on the subject of what happens to software architecture during maintenance activities. There seems to be a consensus that it degrades during the evolution of the software. More recent work shows that this degradation occurs even during development activities: design decisions are either adjusted or forgotten. Some studies have looked into the causes of this degradation, but these have mostly done so at a very high level. This study examines three projects at code level. Three architectural pre-implementation designs are compared with their post-implementation design counterparts, with special attention paid to the causes of the changes. We found many negative changes causing anti-patterns, at the package, class, and method levels. After analysis of the code, we were able to find the specific reasons for the poor design decisions. Although the underlying causes are varied, they can be grouped into three basic categories: knowledge problems, artifact problems, and management problems. This categorization shows that anti-pattern causes are varied and are not all due to the developers. The main conclusion is that promoting awareness of anti-patterns to developers is insufficient to prevent them since some of the causes escape their grasp.
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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 | 0.006 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".