Phuong T. Nguyen

Davide Di Ruscio, Paola Inverardi, Patrizio Migliarini et al.

Privacy and ethics of citizens are at the core of the concerns raised by our increasingly digital society. Profiling users is standard practice for software applications triggering the need for users, also enforced by laws, to properly manage privacy settings. Users need to manage software privacy settings properly to protect personally identifiable information and express personal ethical preferences. AI technologies that empower users to interact with the digital world by reflecting their personal ethical preferences can be key enablers of a trustworthy digital society. We focus on the privacy dimension and contribute a step in the above direction through an empirical study on an existing dataset collected from the fitness domain. We find out which set of questions is appropriate to differentiate users according to their preferences. The results reveal that a compact set of semantic-driven questions (about domain-independent privacy preferences) helps distinguish users better than a complex domain-dependent one. This confirms the study's hypothesis that moral attitudes are the relevant piece of information to collect. Based on the outcome, we implement a recommender system to provide users with suitable recommendations related to privacy choices. We then show that the proposed recommender system provides relevant settings to users, obtaining high accuracy.

Yen-Trang Dang, Thanh-Le Cong, Phuc-Thanh Nguyen et al.

Open-source development has revolutionized the software industry by promoting collaboration, transparency, and community-driven innovation. Today, a vast amount of various kinds of open-source software, which form networks of repositories, is often hosted on GitHub - a popular software development platform. To enhance the discoverability of the repository networks, i.e., groups of similar repositories, GitHub introduced repository topics in 2017 that enable users to more easily explore relevant projects by type, technology, and more. It is thus crucial to accurately assign topics for each GitHub repository. Current methods for automatic topic recommendation rely heavily on TF-IDF for encoding textual data, presenting challenges in understanding semantic nuances. This paper addresses the limitations of existing techniques by proposing Legion, a novel approach that leverages Pre-trained Language Models (PTMs) for recommending topics for GitHub repositories. The key novelty of Legion is three-fold. First, Legion leverages the extensive capabilities of PTMs in language understanding to capture contextual information and semantic meaning in GitHub repositories. Second, Legion overcomes the challenge of long-tailed distribution, which results in a bias toward popular topics in PTMs, by proposing a Distribution-Balanced Loss (DB Loss) to better train the PTMs. Third, Legion employs a filter to eliminate vague recommendations, thereby improving the precision of PTMs. Our empirical evaluation on a benchmark dataset of real-world GitHub repositories shows that Legion can improve vanilla PTMs by up to 26% on recommending GitHubs topics. Legion also can suggest GitHub topics more precisely and effectively than the state-of-the-art baseline with an average improvement of 20% and 5% in terms of Precision and F1-score, respectively.

Juri Di Rocco, Davide Di Ruscio, Claudio Di Sipio et al.

To perform their daily tasks, developers intensively make use of existing resources by consulting open-source software (OSS) repositories. Such platforms contain rich data sources, e.g., code snippets, documentation, and user discussions, that can be useful for supporting development activities. Over the last decades, several techniques and tools have been promoted to provide developers with innovative features, aiming to bring in improvements in terms of development effort, cost savings, and productivity. In the context of the EU H2020 CROSSMINER project, a set of recommendation systems has been conceived to assist software programmers in different phases of the development process. The systems provide developers with various artifacts, such as third-party libraries, documentation about how to use the APIs being adopted, or relevant API function calls. To develop such recommendations, various technical choices have been made to overcome issues related to several aspects including the lack of baselines, limited data availability, decisions about the performance measures, and evaluation approaches. This paper is an experience report to present the knowledge pertinent to the set of recommendation systems developed through the CROSSMINER project. We explain in detail the challenges we had to deal with, together with the related lessons learned when developing and evaluating these systems. Our aim is to provide the research community with concrete takeaway messages that are expected to be useful for those who want to develop or customize their own recommendation systems. The reported experiences can facilitate interesting discussions and research work, which in the end contribute to the advancement of recommendation systems applied to solve different issues in Software Engineering.

Phuong T. Nguyen, Juri Di Rocco, Claudio Di Sipio et al.

Software development activity has reached a high degree of complexity, guided by the heterogeneity of the components, data sources, and tasks. The proliferation of open-source software (OSS) repositories has stressed the need to reuse available software artifacts efficiently. To this aim, it is necessary to explore approaches to mine data from software repositories and leverage it to produce helpful recommendations. We designed and implemented FOCUS as a novel approach to provide developers with API calls and source code while they are programming. The system works on the basis of a context-aware collaborative filtering technique to extract API usages from OSS projects. In this work, we show the suitability of FOCUS for Android programming by evaluating it on a dataset of 2,600 mobile apps. The empirical evaluation results show that our approach outperforms two state-of-the-art API recommenders, UP-Miner and PAM, in terms of prediction accuracy. We also point out that there is no significant relationship between the categories for apps defined in Google Play and their API usages. Finally, we show that participants of a user study positively perceive the API and source code recommended by FOCUS as relevant to the current development context.

Riccardo Rubei, Davide Di Ruscio, Claudio Di Sipio et al.

During the development of a software project, developers often need to upgrade third-party libraries (TPLs), aiming to keep their code up-to-date with the newest functionalities offered by the used libraries. In most cases, upgrading used TPLs is a complex and error-prone activity that must be carefully carried out to limit the ripple effects on the software project that depends on the libraries being upgraded. In this paper, we propose EvoPlan as a novel approach to the recommendation of different upgrade plans given a pair of library-version as input. In particular, among the different paths that can be possibly followed to upgrade the current library version to the desired updated one, EvoPlan is able to suggest the plan that can potentially minimize the efforts being needed to migrate the code of the clients from the library's current release to the target one. The approach has been evaluated on a curated dataset using conventional metrics used in Information Retrieval, i.e., precision, recall, and F-measure. The experimental results show that EvoPlan obtains an encouraging prediction performance considering two different criteria in the plan specification, i.e., the popularity of migration paths and the number of open and closed issues in GitHub for those projects that have already followed the recommended migration paths.

Ludovico Iovino, Phuong T. Nguyen, Amleto Di Salle et al.

Studies on public transportation in Europe suggest that European inhabitants use buses in ca. 56% of all public transport travels. One of the critical factors affecting such a percentage and more, in general, the demand for public transport services, with an increasing reluctance to use them, is their quality. End-users can perceive quality from various perspectives, including the availability of information, i.e., the access to details about the transit and the provided services. The approach proposed in this paper, using innovative methodologies resorting on data mining and machine learning techniques, aims to make available the unavailable data about public transport. In particular, by mining GPS traces, we manage to reconstruct the complete transit graph of public transport. The approach has been successfully validated on a real dataset collected from the local bus system of the city of L'Aquila (Italy). The experimental results demonstrate that the proposed approach and implemented framework are both effective and efficient, thus being ready for deployment.