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Collab-RS: semantic recommendation of external collaborators for projects in software ecosystems

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Abstract

The software development industry has evolved in recent years, presenting new challenges. In this scenario, software ecosystems have emerged as a new development paradigm through which external contributors support software production by providing solutions that complement a common ecosystem platform. Due to the many technologies, frameworks, and domains that an ecosystem can host, many collaborators acquainted with various domain topics and skills have also come into play. Recruiting collaborators becomes complex due to the varying degrees of knowledge and skills each collaborator has and their multiple competencies. There is a need to support the decision-making in the collaborator’s recruitment, using the knowledge related to their skills. This work presents a solution supported by an ontology capable of recommending external collaborators for specific projects. The solution encompasses an architecture based on semantic models and expertise retrieval techniques. The architecture scores the collaborators’ level of knowledge about topics and provides contextual information for the recommendation. Two studies were conducted involving two real software ecosystem platforms (Node.js and E-SECO). Results reveal that our approach can (i) use semantic models and inference mechanisms, (ii) offer context information essential for recruiter decision-making, and (iii) support recruiter’ decision on contributor selection.

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Notes

  1. Software product lines (SPLs), or software product line development, refer to software engineering methods, tools, and techniques for creating a collection of similar software systems from a shared set of software assets using a common means of production.

  2. Global software development (GSD) is multi-site software development with software teams scattered across different places around the world.

  3. A software ecosystem is defined as a set of actors (e.g., software developers, vendors, distributors, complementors) interacting on a shared software market to satisfy consumers’ demands.

  4. A socio-technical system (STS) considers hardware, software, personal, and community requirements. It applies an understanding of the social structures, roles, and rights (the social sciences) to inform the design of systems that involve communities of people and technology.

  5. It is a conceptual data model that includes the capability to express and exchange information, enabling parties to interpret meaning (semantics) from the instances, without needing to know the meta-model. Such semantic models are fact-oriented (as opposed to object-oriented). Facts are typically expressed by binary relations between data elements, whereas higher-order relations are expressed as collections of binary relations. Typically, binary relations have the form of triples: Object-RelationType-Object.

  6. Expertise Retrieval is defined as finding experts in different subject areas and identifying people’s expertise area(s) using computational techniques.

  7. Developers that work on other SECO´s projects.

  8. A SECO platform can be defined as a stable core (such as a smartphone operating system or a music streaming service) that mediates the relationship between a wide range of complements (like apps, games, or songs) and prospective developers, managers, and end-users.

  9. A variability model is a model that captures the commonality and variability of the software product families.

  10. Domain engineering (DE) analyzes software systems for the concepts, notations, and implementation methods and codifies that knowledge in a reusable form.

  11. An ontology encompasses a representation, formal naming, and definition of the categories, properties, and relations between the concepts, data, and entities that substantiate one, many, or all domains of discourse.

  12. The details of a systematic mapping we conducted in this area can be accessed at https://doi.org/10.13140/RG.2.2.30637.72161/1.

  13. https://www.w3.org/OWL/.

  14. https://github.com/marciotojr/collab-rs-SBES/blob/master/SECOn.owl.

  15. https://doi.org/10.13140/RG.2.2.12182.78403.

  16. This example was extracted from our evaluation HR1.

  17. Matchers are software capable of identifying the equivalence between components of an ontology (classes, individuals, properties).

  18. This technology can be replaced if another more suitable technique is available, only compatibility with the endpoints is required.

  19. https://nodejs.org/.

  20. https://v8.dev.

  21. https://www.npmjs.com.

  22. https://developer.github.com/v3/.

  23. GHTorrent is a set of services and tools available on the web that monitors Github’s public events, extracts information from these events, and stores them in databases. http://ghtorrent.org.

  24. https://www.antlr.org.

  25. https://git-scm.com/docs/git-blame.

  26. ANTLR is a parser generator that can be used to read and process structured codes, allowing the user to create an analysis tree representing the code for analysis at a syntactic level. For example, this type of representation allows us to retrieve lines on which the dependencies were called.

  27. In the ANTLR tree representation, the parent element containing the "require" command represents the command line on which require is invoked. Having the element representing the line, we can check which constant or variable the content imported by the command is stored and which package was imported. We can then continue with the file parser, observing the use of this constant or variable. When the constant or variable is used, the line in which its invocation occurred records it to verify who used it.

  28. http://pgcc.github.io/plscience-ecos/.

  29. https://www.biocatalogue.org/

  30. https://www.myexperiment.org/home.

  31. A set of terms composes the query, which must be compared to the textual content directly or indirectly related to the collaborators. The query must contain the terms searched in the tags, titles, descriptions, and other comments about the scientific workflows in the E-SECO scenario.

  32. https://www.genome.jp/kegg/.

  33. Clicking on the external link icon redirects the user to the pages from which this information was extracted, providing more details about the type of information that led to the recommendation. The topic’s provenance is separated according to its type to check the multiple sources of information obtained through the query.

  34. To quote: "The E-SECO platform has a major deficiency today, which is the lack of support for the distributed development of scientific applications. It is easy to specify a distributed experiment where scientists from around the world can collaborate. Based on scientific applications already known to the community, the E-SECO platform can compose scientific experiments. However, when someone wants to develop/use new applications in experiments, the support is minimal, and there is no support for the search for suitable developers. As we can see in the queries’ return, the repositories associated with E-SECO did not help in this search for external developers. These results reinforced the need to evolve the platform considering business and social support. We hope that Collab-RS can assist as the first step in that direction. I look forward to the results achieved by its use".

  35. To quote: "With no doubt the use of Collab-RS improves the results, considering the search for developers specialized in scientific applications related to specific topics, but I expected more outstanding results, to recommend developers related to specific scientific techniques but also that we could identify developers related to specific development technologies, such as java, python, and specific functionalities of scientific applications, which could be easily composed with other applications in a scientific workflow. However, I congratulate the work and the results returned, as it is undoubtedly an important improvement for the E-SECO platform. I also emphasize that I know that the results can be significantly improved by refining the associated repositories’ metadata. In this sense, we must try to create a meta-repository on the E-SECO platform, which stores metadata linked to the applications stored in the associated repositories, to feed Collab-RS’s search with more information, improving its results. Anyway, we have made progress in supporting the search for external developers at E-SECO, but we must continue to improve. Considering the question: If Collab-RS solution can recommend external developers for software projects related to a SECO, I can answer YES, since now, with the use of Collab-RS, we have a mechanism which presents, in detail (context), how the developer recommendation process takes place in E-SECO, but also MAYBE, considering the necessity of more metadata to feed the search process".

  36. Clicking on the external link icon redirects the user to the pages from which this information was extracted, providing more details about the type of information that led to the recommendation. The topic’s provenance is separated according to its type to check the multiple sources of information obtained through the query.

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Acknowledgements

We would like to thank the people who participated in the evaluation.

Funding

This work was partially funded by UFJF/Brazil, CAPES/Brazil, CNPq/Brazil (Grant: 307194/2022-1), and FAPEMIG/Brazil (Grant: APQ-02685-17), (Grant: APQ-02194-18).

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  1. Márcio Oliveira, Regina Braga, Gleiph Ghiotto, José Maria N. David, Fernanda Campos, and Victor Ströele have contributed equally to this work.

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  1. Computer Science Postgraduate Program, Universidade Federal de Juiz de Fora, Juiz de Fora, Brazil

    Márcio Oliveira, Regina Braga, Gleiph Ghiotto, José Maria N. David, Fernanda Campos & Victor Ströele

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Oliveira, M., Braga, R., Ghiotto, G. et al. Collab-RS: semantic recommendation of external collaborators for projects in software ecosystems. Knowl Inf Syst 66, 147–186 (2024). https://doi.org/10.1007/s10115-023-01954-y

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