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Identifying the Mood of a Software Development Team by Analyzing Text-Based Communication in Chats with Machine Learning

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Human-Centered Software Engineering (HCSE 2020)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 12481))

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Abstract

Software development encompasses many collaborative tasks in which usually several persons are involved. Close collaboration and the synchronization of different members of the development team require effective communication. One established communication channel are meetings which are, however, often not as effective as expected. Several approaches already focused on the analysis of meetings to determine the reasons for inefficiency and dissatisfying meeting outcomes. In addition to meetings, text-based communication channels such as chats and e-mails are frequently used in development teams. Communication via these channels requires a similar appropriate behavior as in meetings to achieve a satisfying and expedient collaboration. However, these channels have not yet been extensively examined in research.

In this paper, we present an approach for analyzing interpersonal behavior in text-based communication concerning the conversational tone, the familiarity of sender and receiver, the sender’s emotionality, and the appropriateness of the used language. We evaluate our approach in an industrial case study based on 1947 messages sent in a group chat in Zulip over 5.5 months. Using our approach, it was possible to automatically classify written sentences as positive, neutral, or negative with an average accuracy of 62.97% compared to human ratings. Despite this coarse-grained classification, it is possible to gain an overall picture of the adequacy of the textual communication and tendencies in the group mood.

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Notes

  1. 1.

    https://slack.com/.

  2. 2.

    https://www.skype.com/.

  3. 3.

    https://zulipchat.com/.

  4. 4.

    Note that the analysis of bi-directional communication is questionable due to privacy concerns and personal messages in private chats.

  5. 5.

    Note that both databases are based on the German language as we performed our application in industry (see Sect. 4) in a German-speaking company.

  6. 6.

    More information on Zulip can be found at https://zulipchat.com/.

  7. 7.

    As the number of metrics is quite high, future research should focus on the selection of appropriate metrics.

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Authors and Affiliations

  1. Software Engineering Group, Leibniz University Hannover, Hannover, Germany

    Jil Klünder, Julian Horstmann & Oliver Karras

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  1. Jil Klünder
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  2. Julian Horstmann
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  3. Oliver Karras
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Correspondence to Jil Klünder .

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Editors and Affiliations

  1. Eindhoven University of Technology, Eindhoven, The Netherlands

    Regina Bernhaupt

  2. Politecnico di Bari, Bari, Italy

    Carmelo Ardito

  3. University of Paderborn, Paderborn, Germany

    Stefan Sauer

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Klünder, J., Horstmann, J., Karras, O. (2020). Identifying the Mood of a Software Development Team by Analyzing Text-Based Communication in Chats with Machine Learning. In: Bernhaupt, R., Ardito, C., Sauer, S. (eds) Human-Centered Software Engineering. HCSE 2020. Lecture Notes in Computer Science(), vol 12481. Springer, Cham. https://doi.org/10.1007/978-3-030-64266-2_8

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  • DOI: https://doi.org/10.1007/978-3-030-64266-2_8

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  • Online ISBN: 978-3-030-64266-2

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