Identifying Unmaintained Projects in GitHub

Background: Open source software has an increasing importance in modern software development. However, there is also a growing concern on the sustainability of such projects, which are usually managed by a small number of developers, frequently working as volunteers. Aims: In this paper, we propose an approach to identify GitHub projects that are not actively maintained. Our goal is to alert users about the risks of using these projects and possibly motivate other developers to assume the maintenance of the projects. Method: We train machine learning models to identify unmaintained or sparsely maintained projects, based on a set of features about project activity (commits, forks, issues, etc). We empirically validate the model with the best performance with the principal developers of 129 GitHub projects. Results: The proposed machine learning approach has a precision of 80%, based on the feedback of real open source developers; and a recall of 96%. We also show that our approach can be used to assess the risks of projects becoming unmaintained. Conclusions: The model proposed in this paper can be used by open source users and developers to identify GitHub projects that are not actively maintained anymore.Comment: Accepted at 12th International Symposium on Empirical Software Engineering and Measurement (ESEM), 10 pages, 201

User Review-Based Change File Localization for Mobile Applications

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 localize the potential files to change for addressing the user feedback. In this paper, we propose RISING (Review Integration via claSsification, clusterIng, and linkiNG), an automated approach to support the continuous integration of user feedback via classification, clustering, and linking of user reviews. RISING leverages domain-specific constraint information and semi-supervised learning to group user reviews into multiple fine-grained clusters concerning similar users' requests. Then, by combining the textual information from both commit messages and source code, it automatically localizes potential change files to accommodate the users' requests. Our empirical studies demonstrate that the proposed approach outperforms the state-of-the-art baseline work in terms of clustering and localization accuracy, and thus produces more reliable results.Comment: 15 pages, 3 figures, 8 table

A Review of integrity constraint maintenance and view updating techniques

Two interrelated problems may arise when updating a database. On one hand, when an update is applied to the database, integrity constraints may become violated. In such case, the integrity constraint maintenance approach tries to obtain additional updates to keep integrity constraints satisfied. On the other hand, when updates of derived or view facts are requested, a view updating mechanism must be applied to translate the update request into correct updates of the underlying base facts. This survey reviews the research performed on integrity constraint maintenance and view updating. It is proposed a general framework to classify and to compare methods that tackle integrity constraint maintenance and/or view updating. Then, we analyze some of these methods in more detail to identify their actual contribution and the main limitations they may present.Postprint (published version

Establishing a resource center: A guide for organizations supporting community foundations

Maintaining a resource center such as a library is a central tasks of an association to serve its members, though one of the first to be neglected. WINGS-CF commissioned this guide to assist organizations supporting community foundations to review and organize their resource items, and to propose several classification systems / taxonomies

A Change Support Model for Distributed Collaborative Work

Distributed collaborative software development tends to make artifacts and decisions inconsistent and uncertain. We try to solve this problem by providing an information repository to reflect the state of works precisely, by managing the states of artifacts/products made through collaborative work, and the states of decisions made through communications. In this paper, we propose models and a tool to construct the artifact-related part of the information repository, and explain the way to use the repository to resolve inconsistencies caused by concurrent changes of artifacts. We first show the model and the tool to generate the dependency relationships among UML model elements as content of the information repository. Next, we present the model and the method to generate change support workflows from the information repository. These workflows give us the way to efficiently modify the change-related artifacts for each change request. Finally, we define inconsistency patterns that enable us to be aware of the possibility of inconsistency occurrences. By combining this mechanism with version control systems, we can make changes safely. Our models and tool are useful in the maintenance phase to perform changes safely and efficiently.Comment: 10 pages, 13 figures, 4 table

On consistency maintenance in service discovery

Communication and node failures degrade the ability of a service discovery protocol to ensure Users receive the correct service information when the service changes. We propose that service discovery protocols employ a set of recovery techniques to recover from failures and regain consistency. We use simulations to show that the type of recovery technique a protocol uses significantly impacts the performance. We benchmark the performance of our own service discovery protocol, FRODO against the performance of first generation service discovery protocols, Jini and UPnP during increasing communication and node failures. The results show that FRODO has the best overall consistency maintenance performance