Revisiting Turnover-Induced Knowledge Loss in Software Projects
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
In large software projects, tacit knowledge of the system is threatened by developer turnover. When a developer leaves the project, their knowledge may be lost if the other developers do not understand the design decisions made by the leaving developer. Understanding the source code written by leaving developers thus becomes a burden for their successors. In a previous paper, Rigby et al. reported on a case study of turnover-induced knowledge loss in two large projects, Chromium and a project at Avaya, using risk evaluation methods usually applied to financial systems. They found that the two projects were susceptible to large knowledge losses that are more than three times the average loss. We report on a replication of their study on the Chromium project, as well as seven other large and medium-sized open source projects. We also extended theirwork by studying two variations of the knowledge loss metric, as well as the location and persistence of abandoned files. We found that all projects had a similar knowledge loss probability distribution, but extreme knowledge loss can be more severe than those originally discovered in Chromium and the project at Avaya. We also found that, in the systems under study, abandoned files often remained in the system for long periods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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