Can instability variations warn developers when open-source projects boost?
Alejandro Valdezate, Rafael Capilla, Gregorio Robles, Victor Salamanca

TL;DR
This paper investigates how variations in architecture instability can serve as early warnings for open-source projects transitioning from small to large, community-driven development, aiming to predict instability evolution.
Contribution
It introduces methods to analyze and predict architecture instability in OSS projects during growth phases, filling a gap in understanding instability dynamics over time.
Findings
Preliminary results suggest feasible estimations of instability evolution.
Instability variations correlate with project growth phases.
Potential for early warnings to OSS teams before excessive growth.
Abstract
Although architecture instability has been studied and measured using a variety of metrics, a deeper analysis of which project parts are less stable and how such instability varies over time is still needed. While having more information on architecture instability is, in general, useful for any software development project, it is especially important in Open Source Software (OSS) projects where the supervision of the development process is more difficult to achieve. In particular, we are interested when OSS projects grow from a small controlled environment (i.e., the cathedral phase) to a community-driven project (i.e., the bazaar phase). In such a transition, the project often explodes in terms of software size and number of contributing developers. Hence, the complexity of the newly added features, and the frequency of the commits and files modified may cause significant variations…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSoftware Engineering Research · Software System Performance and Reliability · Scientific Computing and Data Management
