[Paper] Quantifying Competitive Relationships Among Open-Source Software Projects
Source: arXiv - 2602.17131v1
Overview
Open‑source software (OSS) projects don’t evolve in isolation – they constantly compete for contributors, users, and ecosystem “real‑estate.” Takei, Aoki, and Ragkhitwetsagul introduce MIAO (Mutual Impact Analysis of OSS), an automated framework that quantifies these competitive relationships and predicts when a project is likely to stall or die. By borrowing econometric tools from macro‑economics, the authors show that competitive pressure can be measured with surprisingly high accuracy, offering a new lens for developers and maintainers to navigate the fast‑moving OSS landscape.
Key Contributions
- MIAO framework: First application of structural vector autoregressive (SVAR) models and impulse‑response analysis to OSS project interaction data.
- Automated competition detection: Quantifies how activity in one project (e.g., commits, issues) impacts the growth or decline of another.
- High‑accuracy prediction: Identifies projects forced to cease development with up to 81 % accuracy; predicts cessation a year in advance with 77 % accuracy.
- Large‑scale empirical validation: Analyzed 187 OSS project groups spanning web development, deep learning, and other rapidly evolving domains.
- Feature set for downstream models: Provides interpretable metrics (e.g., “competitive pressure score”) that can be fed into existing project health dashboards.
Methodology
- Data collection – Mined public repositories (GitHub, GitLab) for activity logs (commits, pull requests, issue comments) across 187 related project groups.
- Time‑series construction – Built weekly activity vectors for each project, treating each vector as a macro‑economic indicator (e.g., “output”).
- Structural Vector Autoregression (SVAR) – Captures how the current activity of every project depends on its own past activity and the past activity of its peers.
- Impulse‑Response Functions (IRFs) – By injecting a “shock” (e.g., a sudden surge of commits) into one project’s time series, IRFs trace the ripple effect on all other projects over subsequent weeks.
- Competitive influence scoring – Aggregates the magnitude and sign of the IRF response into a “competition score” indicating whether a project is a net aggressor or victim.
- Predictive modeling – Uses the competition scores as features for a binary classifier (random forest) that predicts whether a project will stop development within the next 12 months.
All steps are fully automated, requiring only publicly available repository metadata.
Results & Findings
| Metric | Outcome |
|---|---|
| Cessation detection (retrospective) | 81 % accuracy in flagging projects that actually stopped development due to competitive pressure. |
| One‑year‑ahead prediction | 77 % accuracy (AUC ≈ 0.84) for forecasting cessation before it happens. |
| Feature importance | Competition scores contributed >30 % of the predictive power, outperforming traditional health indicators (e.g., star count, issue backlog). |
| Domain robustness | Similar performance across diverse ecosystems (web frameworks, ML libraries, dev‑ops tools). |
In plain terms, MIAO can tell you not only who is threatening a project, but also how strongly and how soon that threat may materialize.
Practical Implications
- Roadmap planning – Maintainers can prioritize features that differentiate their project from a high‑impact competitor, reducing the risk of being “out‑competed.”
- Resource allocation – Companies sponsoring OSS can allocate developer time to projects showing low competitive pressure, maximizing ROI.
- Ecosystem monitoring – Platform providers (GitHub, GitLab) could integrate MIAO scores into health dashboards, alerting owners before a project’s activity collapses.
- Investment decisions – Venture funds and corporate R&D groups can use the competition metrics to assess the longevity of OSS components they depend on.
- Community building – Early detection of competitive stress enables proactive community outreach (e.g., mentorship, contributor incentives) to keep a project alive.
Overall, MIAO turns a traditionally qualitative notion—“this project is losing ground”—into a data‑driven signal that developers can act upon.
Limitations & Future Work
- Data granularity – Relies on weekly activity aggregates; sudden micro‑level events (e.g., a security breach) may be missed.
- Causality vs. correlation – While SVAR imposes structural assumptions, true causal mechanisms (e.g., licensing changes) are not directly observed.
- Scope of ecosystems – Focused on relatively popular, well‑documented OSS groups; niche projects with sparse data may yield noisy scores.
- Model extensions – Future work could incorporate additional signals (download statistics, dependency graphs, social media sentiment) and explore deep‑learning time‑series models for richer dynamics.
Despite these constraints, MIAO offers a compelling first step toward systematic, predictive insight into the competitive life‑cycle of open‑source software.
Authors
- Yuki Takei
- Toshiaki Aoki
- Chaiyong Ragkhitwetsagul
Paper Information
- arXiv ID: 2602.17131v1
- Categories: cs.SE
- Published: February 19, 2026
- PDF: Download PDF