arXiv subjects

Claudio Di Sipio

Publications and source records attributed to Claudio Di Sipio.

24 records · Page 2Linked to original sources

arXiv2023

Dealing with Popularity Bias in Recommender Systems for Third-party Libraries: How far Are We?

Recommender systems for software engineering (RSSEs) assist software engineers in dealing with a growing information overload when discerning alternative development solutions. While RSSEs are becoming more and more effective in suggesting handy recommendations, they tend to suffer from popularity bias, i.e., favoring items that are relevant mainly because several developers are using them. While this rewards artifacts that are likely more reliable and well-documented, it would also mean that missing artifacts are rarely used because they are very specific or more recent. This paper studies popularity bias in Third-Party Library (TPL) RSSEs. First, we investigate whether state-of-the-art research in RSSEs has already tackled the issue of popularity bias. Then, we quantitatively assess four existing TPL RSSEs, exploring their capability to deal with the recommendation of popular items. Finally, we propose a mechanism to defuse popularity bias in the recommendation list. The empirical study reveals that the issue of dealing with popularity in TPL RSSEs has not received adequate attention from the software engineering community. Among the surveyed work, only one starts investigating the issue, albeit getting a low prediction performance.

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arXiv2022

GitRanking: A Ranking of GitHub Topics for Software Classification using Active Sampling

GitHub is the world's largest host of source code, with more than 150M repositories. However, most of these repositories are not labeled or inadequately so, making it harder for users to find relevant projects. There have been various proposals for software application domain classification over the past years. However, these approaches lack a well-defined taxonomy that is hierarchical, grounded in a knowledge base, and free of irrelevant terms. This work proposes GitRanking, a framework for creating a classification ranked into discrete levels based on how general or specific their meaning is. We collected 121K topics from GitHub and considered $60\%$ of the most frequent ones for the ranking. GitRanking 1) uses active sampling to ensure a minimal number of required annotations; and 2) links each topic to Wikidata, reducing ambiguities and improving the reusability of the taxonomy. Our results show that developers, when annotating their projects, avoid using terms with a high degree of specificity. This makes the finding and discovery of their projects more challenging for other users. Furthermore, we show that GitRanking can effectively rank terms according to their general or specific meaning. This ranking would be an essential asset for developers to build upon, allowing them to complement their annotations with more precise topics. Finally, we show that GitRanking is a dynamically extensible method: it can currently accept further terms to be ranked with a minimum number of annotations ($\sim$ 15). This paper is the first collective attempt to build a ground-up taxonomy of software domains.

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arXiv2022

MemoRec: A Recommender System for Assisting Modelers in Specifying Metamodels

Model Driven Engineering (MDE) has been widely applied in software development, aiming to facilitate the coordination among various stakeholders. Such a methodology allows for a more efficient and effective development process. Nevertheless, modeling is a strenuous activity that requires proper knowledge of components, attributes, and logic to reach the level of abstraction required by the application domain. In particular, metamodels play an important role in several paradigms, and specifying wrong entities or attributes in metamodels can negatively impact on the quality of the produced artifacts as well as other elements of the whole process. During the metamodeling phase, modelers can benefit from assistance to avoid mistakes, e.g., getting recommendations like meta-classes and structural features relevant to the metamodel being defined. However, suitable machinery is needed to mine data from repositories of existing modeling artifacts and compute recommendations. In this work, we propose MemoRec, a novel approach that makes use of a collaborative filtering strategy to recommend valuable entities related to the metamodel under construction. Our approach can provide suggestions related to both metaclasses and structured features that should be added in the metamodel under definition. We assess the quality of the work with respect to different metrics, i.e., success rate, precision, and recall. The results demonstrate that MemoRec is capable of suggesting relevant items given a partial metamodel and supporting modelers in their task.

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arXiv2022

Providing Upgrade Plans for Third-party Libraries: A Recommender System using Migration Graphs

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.

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arXiv2021

Development of recommendation systems for software engineering: the CROSSMINER experience

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.

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arXiv2021

Recommending API Function Calls and Code Snippets to Support Software Development

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.

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