Organizational volatility and post-release defects: a replication case study using data from Google Chrome
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The quality of software projects is affected by developer turnover. Mockus studied organizational volatility in the context a large switching software project at Avaya. We replicate his model of the impact of organizational volatility on post-release defects. At the time of Mockus's study, Avaya was experimenting with outsourcing and layoffs were prevalent. In contrast, we study volatility on the Chrome web-browser, which is growing rapidly in terms of popularity and team size. Where possible, we use the same factors as Mockus: the number of co-owners, the number of developers joining and leaving the organization, the number of co-changing directories, developer experience and, instead of LOCs, the churn. Our investigation is conducted at the directory instead of the file level. The control variables, including churn, number of co-owners, and expertise all conform with the consensus in the literature that more changes, more co-owners, and lower expertise lead to an increase in customer reported post-release defects. After normalizing by the highly correlated number of co-owners, the number of developers who leave and join both reduce the number of post-release defects. We discuss this unexpected result.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it