Dependency Smells in JavaScript Projects
Bibliographic record
Abstract
Dependency management in modern software development poses many challenges for developers who wish to stay up to date with the latest features and fixes whilst ensuring backwards compatibility. Project maintainers have opted for varied, and sometimes conflicting, approaches for maintaining their dependencies. Opting for unsuitable approaches can introduce bugs and vulnerabilities into the project, introduce breaking changes, cause extraneous installations, and reduce dependency understandability, making it harder for others to contribute effectively. In this paper, we empirically examine evidence of recurring dependency management issues (dependency smells). We look at the commit data for a dataset of 1,146 active JavaScript repositories to catalog, quantify and understand dependency smells. Through a series of surveys with practitioners, we identify and quantify seven dependency smells with varying degrees of popularity and investigate why they are introduced throughout project history. Our findings indicate that dependency smells are prevalent in JavaScript projects with two or more distinct smells appearing in 80 percent of the projects, but they generally infect a minority of a project’s dependencies. Our observations show that the number of dependency smells tend to increase over time. Practitioners agree that dependency smells bring about many problems including security threats, bugs, dependency breakage, runtime errors, and other maintenance issues. These smells are generally introduced as developers react to dependency misbehaviour and the shortcomings of thenpmecosystem.
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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 | 0.007 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".