Online collaboration platforms such as GitHub have provided software developers with the ability to easily reuse and share code between repositories. With clone-and-own and forking becoming prevalent, maintaining these shared files is important, especially for keeping the most up-to-date version of reused code. Different to related work, we propose the concept of meta-maintenance -- i.e., tracking how the same files evolve in different repositories with the aim to provide useful maintenance opportunities to those files. We conduct an exploratory study by analyzing repositories from seven different programming languages to explore the potential of meta-maintenance. Our results indicate that a majority of active repositories on GitHub contains at least one file which is also present in another repository, and that a significant minority of these files are maintained differently in the different repositories which contain them. We manually analyzed a representative sample of shared files and their variants to understand which changes might be useful for meta-maintenance. Our findings support the potential of meta-maintenance and open up avenues for future work to capitalize on this potential.
Maintenance Signals in AI-Assisted GitHub Repositories: Evidence from GenAI Adopters
Nara Institute of Science and Technology（奈良先端科学技术大学院大学）；Wakayama University（和歌山大学）；The University of Osaka（大阪大学）；University of British Columbia, Okanagan Campus（不列颠哥伦比亚大学奥肯那根校区）
We advocate for a paradigm shift in supporting free/libre and open source software (FLOSS) ecosystem maintenance, from focusing on individual projects to monitoring a whole organic system of the entire FLOSS ecosystem, which we call software meta-maintenance. We discuss challenges of building a global source code management system, a global issue management system, and FLOSS human capital index, based on the blockchain technologies.
Maintenance is a dominant component of software cost, and localizing reported defects is a significant component of maintenance. We propose a scalable approach that leverages the natural language present in both defect reports and source code to identify files that are potentially related to the defect in question. Our technique is language-independent and does not require test cases. The approach represents reports and code as separate structured documents and ranks source files based on a document similarity metric that leverages inter-document relationships. We evaluate the fault-localizati…
In the current mobile app development, novel and emerging DevOps practices (e.g., Continuous Delivery, Integration, and user feedback analysis) and tools are becoming more widespread. For instance, the integration of user feedback (provided in the form of user reviews) in the software release cycle represents a valuable asset for the maintenance and evolution of mobile apps. To fully make use of these assets, it is highly desirable for developers to establish semantic links between the user reviews and the software artefacts to be changed (e.g., source code and documentation), and thus to loca…
During software maintenance and evolution, developers need to deal with a large number of change requests by modifying existing code or adding code into the system. An efficient tackling of change request calls for an accurate localising of software changes, i.e. identifying which code are problematic and where new files should be added for any type of change request at hand, such as a bug report or a feature request. Existing automatic techniques for this change localisation problem are limited in two aspects: on the one hand, they are only limited to tackle a specific type of change request;…
Build systems are an essential part of modern software engineering projects. As software projects change continuously, it is crucial to understand how the build system changes because neglecting its maintenance can lead to expensive build breakage. Recent studies have investigated the (co-)evolution of build configurations and reasons for build breakage, but they did this only on a coarse grained level. In this paper, we present BUILDDIFF, an approach to extract detailed build changes from MAVEN build files and classify them into 95 change types. In a manual evaluation of 400 build changing co…
