An Empirical Study of Dependency Downgrades in the npm Ecosystem
Bibliographic record
Abstract
In a software ecosystem, a dependency relationship enables aclientpackage to reuse a certain version of aproviderpackage. Packages in a software ecosystem often release versions containing bug fixes, new functionalities, and security enhancements. Hence, updating the provider version is an important maintenance task for client packages. Despite the number of investigations about dependency updates, there is a lack of studies about dependency downgrades in software ecosystems. A downgrade indicates that the adopted version of a provider package is not suitable to the client package at a certain moment. In this paper, we investigate downgrades in the${\sf npm}$ecosystem. We address three research questions. In our first RQ, we provide a list of the reasons behind the occurrence of downgrades. Our manual analysis of the artifacts (e.g., release notes and commit messages) of a package code repository identified two categories of downgrades according to their rationale: reactive and preventive. The reasons behind reactive downgrades are defects in a specific version of a provider, unexpected feature changes in a provider, and incompatibilities. In turn, preventive downgrades are an attempt to avoid issues in future releases. In our second RQ, we investigate how the versioning of dependencies is modified when a downgrade occurs. We observe that 49 percent of the downgrades are performed by replacing a range of acceptable versions of a provider by a specific old version. This observation suggests that client packages have the tendency to become more conservative regarding the update of their providers after a downgrade. Also, 48 percent of the downgrades reduce the provider version by a minor level (e.g., from 2.1.0 to 2.0.0). This observation indicates that client packages in${\sf npm}$should be cautious when updating minor releases of the provider (e.g., by prioritizing tests). Finally, in our third RQ we observe that 50 percent of the downgrades are performed at a rate that is 2.6 times as slow as the median time-between-releases of their associated client packages. We also observe that downgrades that follow an explicit update of a provider package occur faster than downgrades that follow an implicit update. Explicit updates occur when the provider is updated by means of an explicit change to the versioning specification (i.e., the string used by client packages to define the provider version that they are willing to adopt). We conjecture that, due to the controlled nature of explicit updates, it is easier for client packages to identify the provider that is associated with the problem that motivated the downgrade.
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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 | 0.016 | 0.151 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".