Reasons and drawbacks of using trivial npm packages: the developers' perspective
Bibliographic record
Abstract
Code reuse is traditionally seen as good practice. Recent trends have pushed the idea of code reuse to an extreme, by using packages that implement simple and trivial tasks, which we call ‘trivial packages’. A recent incident where a trivial package led to the breakdown of some of the most popular web applications such as Facebook and Netflix, put the spotlight on whether using trivial packages should be encouraged. Therefore, in this research, we mine more than 230,000 npm packages and 38,000 JavaScript projects in order to study the prevalence of trivial packages. We found that trivial packages are common, making up 16.8% of the studied npm packages. We performed a survey with 88 Node.js developers who use trivial packages to understand the reasons for and drawbacks of their use. We found that trivial packages are used because they are perceived to be well-implemented and tested pieces of code. However, developers are concerned about maintaining and the risks of breakages due to the extra dependencies trivial packages introduce.
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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 | 0.032 | 0.148 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".