Observing Custom Software Modifications: A Quantitative Approach of Tracking the Evolution of Patch Stacks
Ralf Ramsauer, Daniel Lohmann, Wolfgang Mauerer

TL;DR
This paper introduces a quantitative methodology to analyze the evolution and maintenance effort of patch stacks in open-source software, addressing challenges in long-term management of custom modifications.
Contribution
It presents a novel systematic approach to track and evaluate the evolution, integrability, and maintainability of patch stacks over time.
Findings
Provides a framework for quantitative analysis of patch stack evolution
Estimates the engineering effort needed for long-term patch maintenance
Facilitates statistical and actionable insights for OSS customization
Abstract
Modifications to open-source software (OSS) are often provided in the form of "patch stacks" - sets of changes (patches) that modify a given body of source code. Maintaining patch stacks over extended periods of time is problematic when the underlying base project changes frequently. This necessitates a continuous and engineering-intensive adaptation of the stack. Nonetheless, long-term maintenance is an important problem for changes that are not integrated into projects, for instance when they are controversial or only of value to a limited group of users. We present and implement a methodology to systematically examine the temporal evolution of patch stacks, track non-functional properties like integrability and maintainability, and estimate the eventual economic and engineering effort required to successfully develop and maintain patch stacks. Our results provide a basis for…
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.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSoftware Engineering Research · Open Source Software Innovations · Software Reliability and Analysis Research
