Helping or not Helping? Why and How Trivial Packages Impact the npm Ecosystem
Bibliographic record
Abstract
Developers often share their code snippets by packaging them and making them available to others through software packages. How much a package does and how big it is can be seen as positive or negative. Recent studies showed that many packages that exist in the npm ecosystem are trivial and may introduce high dependency overhead. Hence, one question that arises is why developers choose to publish these trivial packages. Therefore, in this paper, we perform a developer-centered study to empirically examine why developers choose to publish such trivial packages. Specifically, we ask 1) why developers publish trivial packages, 2) what they believe to be the possible negative impacts of these packages, and 3) how such negative issues can be mitigated. The survey response of 59 JavaScript developers who publish trivial npm packages showed that the main reasons for publishing these trivial packages are to provide <em>reusable components</em>, <em>testing</em> & <em>documentation</em>, and <em>separation of concerns</em>. Even the developers who publish these trivial packages admitted to having issues when they publish such packages, which include the <em>maintenance of multiple packages</em>, <em>dependency hell</em>, <em>finding the right package</em>, and the <em>increase of duplicated packages</em> in the ecosystems. Furthermore, we found that the majority of the developers suggested grouping these trivial packages to cope with the problems associated with publishing them. Then, to quantitatively investigate the impact of these trivial packages on the npm ecosystem and its users, we examine grouping these trivial packages. We found that if trivial packages that are always used together are grouped, the ecosystem can reduce the number of dependencies by approximately 13%. Our findings shed light on the impact of publishing trivial packages and show that ecosystems and developer communities need to rethink their publishing policies since it can negatively impact the developers and the entire ecosystem. The published data set contains the following: List of identified trivial npm packages. The survey questions. The developers' responses to the survey. The results of the co-usage analysis of trivial npm packages.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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; both teacher heads agree on what is shown here.
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".