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Assisting Mentors in Selecting Newcomers’ Next Task in Software Product Lines: A Recommender System Approach | SpringerLink
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Assisting Mentors in Selecting Newcomers’ Next Task in Software Product Lines: A Recommender System Approach

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Advanced Information Systems Engineering (CAiSE 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13295))

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Abstract

Onboarding (i.e., the process of incorporating new people) is relevant because it introduces employees to their role, the company’s culture, and what the company has to offer. Onboarding is then dependent on the company’s culture and practices. When it comes to software development, these practices include the methods, the tools or the developers’ organigram. Accordingly, there is not a one-size-fits-all onboarding, rather this procedure needs to be tuned for the practice at hand. This work tackles the specifics brought about by Software Product Line Engineering w.r.t. traditional software development, namely: larger code base, larger code variability, and larger and more heterogeneous teams. Specifically, this works advocates for feature-centric onboarding. Features (i.e., functional characteristics that are visible for a user) already play a key role throughout the SPL lifecycle. In this context, we advocate for defining the onboarding process as a journey where milestones are equated with features. Unfortunately, finding the most appropriate feature for a newcomer, if conducted manually by mentors, would be time-consuming, given the sheer number of features. To face this problem, we advocate for Recommender Systems based on the similarity between the feature’s codebase and the code previously explored by the newcomer. To this end, we resort to Topic Modeling, and specifically, Latent Dirichlet Allocation. We provide proof-of-concept through RecomMentor, a recommender system for pure-variants as the variability management system. RecomMentor is put to test against ranking metrics of the Information Retrieval literature. The first evaluation suggests that LDA could be an appropriate technique, paving the way towards using Recommender Systems in feature-based onboarding scenarios.

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Notes

  1. 1.

    WACline’s source code is available at https://github.com/onekin/WacLine.

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Acknowledgments

This work is supported by the Spanish Ministry of Science and Innovation (RTI2018-099818-B-I00). R. Medeiros enjoys a doctoral grant from the Ministry of Science and Innovation.

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Authors and Affiliations

  1. University of the Basque Country (UPV/EHU), San Sebastián, Spain

    Raul Medeiros & Oscar Díaz

Authors
  1. Raul Medeiros
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  2. Oscar Díaz
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Corresponding author

Correspondence to Raul Medeiros .

Editor information

Editors and Affiliations

  1. Department of Service and Information System Engineering (ESSI), Universitat Politècnica de Catalunya, Barcelona, Spain

    Xavier Franch

  2. Ghent University, Gent, Belgium

    Geert Poels

  3. Ghent University, Gent, Belgium

    Frederik Gailly

  4. KU Leuven, Leuven, Belgium

    Monique Snoeck

A The Oracle

A The Oracle

The WACline oracle used in the evaluation consists of a Feature x Feature matrix (of all the optional features in WACline) where cells indicate similarity values between the optional features of the SPL, values go from 0 (totally dissimilar) to 3 (identical) (check Table 4 for an example). Two developers were involved in the construction process. The process follows: (1) an oracle is provided by each of the developers, (2) both oracles are disclosed, and (3) an agreement on discrepancies is reached.

Table 4. An excerpt of the WACline oracle. The oracle is available at: https://tinyurl.com/fep4jyhs.
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Medeiros, R., Díaz, O. (2022). Assisting Mentors in Selecting Newcomers’ Next Task in Software Product Lines: A Recommender System Approach. In: Franch, X., Poels, G., Gailly, F., Snoeck, M. (eds) Advanced Information Systems Engineering. CAiSE 2022. Lecture Notes in Computer Science, vol 13295. Springer, Cham. https://doi.org/10.1007/978-3-031-07472-1_27

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  • DOI: https://doi.org/10.1007/978-3-031-07472-1_27

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