There’s no Such Thing as a Free Lunch: Lessons Learned from Exploring the Overhead Introduced by the Greenkeeper Dependency Bot in Npm
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
Dependency management bots are increasingly being used to support the software development process, for example, to automatically update a dependency when a new version is available. Yet, human intervention is often required to either accept or reject any action or recommendation the bot creates. In this article, our objective is to study the extent to which dependency management bots create additional, and sometimes unnecessary, work for their users. To accomplish this, we analyze 93,196 issue reports opened by Greenkeeper , a popular dependency management bot used in open source software projects in the npm ecosystem. We find that Greenkeeper is responsible for half of all issues reported in client projects, inducing a significant amount of overhead that must be addressed by clients, since many of these issues were created as a result of Greenkeeper taking incorrect action on a dependency update (i.e., false alarms). Reverting a broken dependency update to an older version, which is a potential solution that requires the least overhead and is automatically attempted by Greenkeeper , turns out to not be an effective mechanism. Finally, we observe that 56% of the commits referenced by Greenkeeper issue reports only change the client’s dependency specification file to resolve the issue. Based on our findings, we argue that dependency management bots should (i) be configurable to allow clients to reduce the amount of generated activity by the bots, (ii) take into consideration more sources of information than only the pass/fail status of the client’s build pipeline to help eliminate false alarms, and (iii) provide more effective incentives to encourage clients to resolve dependency issues.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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