Developer-Centric Knowledge Mining from Large Open-Source Software Repositories (CROSSMINER)

Bagnato, Alessandra; Barmpis, Konstantinos; Bessis, Nik; Cabrera-Diego, Luis Adrián; Rocco, Juri Di; Ruscio, Davide Di; Gergely, Tamás; Hansen, Scott D.; Kolovos, Dimitrios S.; Krief, Philippe; Korkontzelos, Ioannis; Laurière, Stéphane; Fuente, Jose Manrique Lopez de la; Maló, Pedro; Paige, Richard F.; Spinellis, Diomidis; Thomas, Cedric; Vinju, Jurgen

doi:10.1007/978-3-319-74730-9_33

Cited by 6 publications

(2 citation statements)

References 15 publications

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“…Their approach, however neglected several semantic-based repositories when they built the component-based and user-based profiles. Amongst the conducted exertions in constructing knowledge-based recommender systems, CROSSMINER initiative [34] established a large-scale project where authors automatically collect resources and components from various open source repositories and delivering them to the developers in terms of recommendations using built knowledge-based RS. The outcomes of this initiative are depicted in [12,35,36].…”

Section: Related Workmentioning

confidence: 99%

Toward a Knowledge-based Personalised Recommender System for Mobile App Development

Abu-Salih¹,

Alsawalqah²,

Elshqeirat³

et al. 2021

jucs

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Over the last few years, the arena of mobile application development has expanded considerably beyond the demand of the world's software markets. With the growing number of mobile software companies and the increasing sophistication of smartphone technology, developers have been establishing several categories of applications on dissimilar platforms. However, developers confront several challenges when undertaking mobile application projects. In particular, there is a lack of consolidated systems that can competently, promptly and efficiently provide developers with personalised services. Hence, it is essential to develop tailored systems that can recommend appropriate tools, IDEs, platforms, software components and other correlated artifacts to mobile application developers. This paper proposes a new recommender system framework comprising a robust set of techniques that are designed to provide mobile app developers with a specific platform where they can browse and search for personalised artifacts. In particular, the new recommender system framework comprises the following functions: (i) domain knowledge inference module: including various semantic web technologies and lightweight ontologies; (ii) profiling and preferencing: a new proposed time- aware multidimensional user modelling; (iii) query expansion: to improve and enhance the retrieved results by semantically augmenting users’ query; and (iv) recommendation and information filtration: to make use of the aforementioned components to provide personalised services to the designated users and to answer a user’s query with the minimum mismatches.

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Section: Related Workmentioning

confidence: 99%

Toward a Knowledge-based Personalised Recommender System for Mobile App Development

Abu-Salih¹,

Alsawalqah²,

Elshqeirat³

et al. 2021

jucs

View full text Add to dashboard Cite

show abstract

“… There are many other tools providing different kinds of comprehensive views of software development. Some of them are: Complicity ( Neu et al, 2011 ), RepoGrams ( Rozenberg et al, 2016 ), CROSSMINER ( Bagnato et al, 2017 ), Kibble ( Apache, 2022 ), SmartSHARK ( Trautsch et al, 2017 ), Augur ( Goggins, 2022 ), etc. …”

Section: Related Workmentioning

confidence: 99%

GrimoireLab: A toolset for software development analytics

Dueñas¹,

Cosentino²,

González-Barahona

et al. 2021

PeerJ Computer Science

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Background After many years of research on software repositories, the knowledge for building mature, reusable tools that perform data retrieval, storage and basic analytics is readily available. However, there is still room to improvement in the area of reusable tools implementing this knowledge. Goal To produce a reusable toolset supporting the most common tasks when retrieving, curating and visualizing data from software repositories, allowing for the easy reproduction of data sets ready for more complex analytics, and sparing the researcher or the analyst of most of the tasks that can be automated. Method Use our experience in building tools in this domain to identify a collection of scenarios where a reusable toolset would be convenient, and the main components of such a toolset. Then build those components, and refine them incrementally using the feedback from their use in both commercial, community-based, and academic environments. Results GrimoireLab, an efficient toolset composed of five main components, supporting about 30 different kinds of data sources related to software development. It has been tested in many environments, for performing different kinds of studies, and providing different kinds of services. It features a common API for accessing the retrieved data, facilities for relating items from different data sources, semi-structured storage for easing later analysis and reproduction, and basic facilities for visualization, preliminary analysis and drill-down in the data. It is also modular, making it easy to support new kinds of data sources and analysis. Conclusions We present a mature toolset, widely tested in the field, that can help to improve the situation in the area of reusable tools for mining software repositories. We show some scenarios where it has already been used. We expect it will help to reduce the effort for doing studies or providing services in this area, leading to advances in reproducibility and comparison of results.

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Application of Computational Linguistics Techniques for Improving Software Quality

Boudeffa

Abherve

Bagnato

et al. 2019

Product-Focused Software Process Improvement

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Developer-Centric Knowledge Mining from Large Open-Source Software Repositories (CROSSMINER)

Cited by 6 publications

References 15 publications

Toward a Knowledge-based Personalised Recommender System for Mobile App Development

Toward a Knowledge-based Personalised Recommender System for Mobile App Development

GrimoireLab: A toolset for software development analytics

Application of Computational Linguistics Techniques for Improving Software Quality

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