Interface-Variant Dynamics in Software Ecosystems: Resolver-Induced Selection and Adoption in Package Graphs

2026-06-30

AI Summary

This study addresses the challenge of characterizing the propagation and evolutionary dynamics of interface variants within package dependency graphs in distributed software ecosystems—a limitation of traditional interface compatibility research. The authors introduce, for the first time, the concept of selection coefficients from population genetics into software ecosystem analysis. By integrating package graph mining, 2,100 parser probe experiments, and conflict probability modeling, they develop a non-neutral evolutionary model that delineates parser rules as diagnostic signals versus independent selective pressures. Leveraging absorbing Markov processes, directional permutation null models, and time-series predictive validation (via Brier score and AUC), the work presents the first reproducible framework for dynamically assessing interface variants. Empirical results demonstrate that parser-derived directionality significantly influences variant adoption (MAE: 0.07 vs. 0.43, p=0.002), whereas purely temporal features fail to surpass baseline performance.

Abstract

Compatibility research usually treats an interface change as a local writer-reader decision. Distributed software stacks make that decision population structured: an RPC, telemetry, middleware, or service-contract variant is introduced by one provider release and then spreads, stalls, or is mediated across consumers, transitive dependencies, and resolver rules. This paper asks when that observation is a load-bearing software-engineering estimator rather than evolutionary relabeling. We mine interface histories, audit npm, Maven Central, PyPI, and crates.io package graphs, execute 2100 package-manager resolver probes, estimate an ecosystem-specific selection coefficient $s$ from clean conflict probabilities, and use that measured $s$ to forward evaluate a pairwise-comparison absorbing process on the observed package graph. We separate three evidential roles. Fixation is a forward evaluation, not independent evidence: once $s$ is measured, deviation from $1/N$ follows mechanically from the non-neutral process. Checker-derived direction carries adoption signal: a direction-permutation null gives checker-direction gap MAE 0.07 versus null median 0.43 ($p=0.002$). But because that direction is derived from the same boundary state whose admitting frequency is predicted, it is a diagnostic rather than an orthogonal selection test. The stricter checker-free temporal test asks whether early resolver-channel features predict later blocked-to-admitted flips; in this snapshot they do not beat age-only (Brier 0.28 versus 0.24, AUC 0.51 versus 0.54). The result is a reproducible estimator audit for interface-variant dynamics in distributed package graphs, showing where resolver evidence becomes population input and where the current registry data still fail to close the resolver-to-adoption loop.

Problem

• interface-variant dynamics
• resolver-induced selection
• package graphs
• software ecosystems
• adoption

Innovation

• interface-variant dynamics
• package graph
• resolver-induced selection
• selection coefficient
• software ecosystem evolution