On the Untriviality of Trivial Packages: An Empirical Study of npm JavaScript Packages
Bibliographic record
Abstract
Nowadays, developing software would be unthinkable without the use of third-party packages. Although such code reuse helps to achieve rapid continuous delivery of software to end-users, blindly reusing code has its pitfalls. For example, prior work has investigated the rationale for using packages that implement simple functionalities, known as trivial packages (i.e., in terms of the code size and complexity). This prior work showed that although these trivial packages were simple, they were popular and prevalent in the npm ecosystem. This popularity and prevalence of trivial packages peaked our interest in questioning the ‘triviality of trivial packages’. To better understand and examine the triviality of trivial packages, we mine a large set of JavaScript projects that use trivial npm packages and evaluate their relative centrality. Specifically, we evaluate the triviality from two complementary points of view: based on project usage and ecosystem usage of these trivial packages. Our result shows that trivial packages are being used in central JavaScript files of a software project. Additionally, by analyzing all external package API calls in these JavaScript files, we found that a high percentage of these API calls are attributed to trivial packages. Therefore, these packages play a significant role in JavaScript files. Furthermore, in the package dependency network, we observed that 16.8 percent packages are trivial and in some cases removing a trivial package can impact approximately 29 percent of the ecosystem. Overall, our finding indicates that although smaller in size and complexity, trivial packages are highly depended on packages by JavaScript projects. Additionally, our study shows that although they might be called trivial, nothing about trivial packages is trivial.
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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 | 0.009 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".