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IIAG: a data-driven and theory-inspired approach for advising how to interact with new remote collaborators in OSS teams

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Abstract

Open source software development (OSS) team members often need to figure out how to initiate a collaboration with a new remote collaborator. An inappropriate strategy could lead to failures in developing cooperation. In this paper, we propose an approach and corresponding intelligent system called IIAG (Initial Interaction Assistant based on Game theory analytics), which identifies and advises its users about strategies for initial interactions with new remote collaborators. IIAG integrates game theory, decision models, and social factors with the collaborative traces mined from empirical project data to achieve this goal. When a user seeks IIAG’s advice, it simulates an individual’s decision processes to find the strategies that yield the best outcomes. Thus, it can advise proper strategies for users. IIAG is evaluated extensively. We design and perform virtual experiments to evaluate IIAG with empirical data collected from three large open source projects. The results show that IIAG can identify the payoff-optimal strategy with over 80% accuracy. We also conduct a lightweight user study to evaluate the IIAG’s usefulness from the potential users’ perspective. The results are also promising. Thus, IIAG can help OSS team members in making informed decisions about interacting with new remote collaborators.

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Notes

  1. https://www.openstack.org/assets/reports/OpenStack-AnnualReport2017.pdf.

  2. Purposefully creating a “welcoming” environment has been common practices in many OSS projects (Alami et al. 2020; Silva et al. 2017; Tourani et al. 2017). While it helps attract more newcomers, it may also make newcomers form wrong expectations towards projects and their members.

  3. The primary justification of accessing the work item dependency is to identify the defectors who are supposed to work together but not actually collaborate, which also indicates a community does function efficiently (Tamburri et al. 2016).

  4. Let us have a look at a concrete example. A developer, Bob, sends a message to a newcomer he has never worked with before. The message says: “Hey men, I don’t think you’re qualified, don’t touch my code.” Sentiment/opinion analysis will label this message as “negative”, which indicates a “defect” strategy taken by Bob.

  5. In practical operationalization of “never reply”, we set the timeframe as one calendar month. If no reply in one calendar month, we assume that the request would not be replied to.

  6. In our empirical dataset, there are dozens of such examples. An extreme case in Apache Lucene is that a developer made some sexist comments to a newly joined female developer from Eastern Europe.

  7. We ignore the status differences resulting from different locations.

  8. Agent-based simulations “capture the behaviors of complex adaptive systems by modeling the behavior of the individuals who comprise them” (North and Macal 2007).

  9. https://asterixdb.ics.uci.edu/, 09/2009–12/2014.

  10. https://lucene.apache.org/, 04/2005–12/2014.

  11. https://www.chromium.org/chromium-os/ (11/2009–04/2011. We do not include all available Chromium OS data for its IRC was heavily “polluted” by end users from the release of Chromebook in 05/2011.

  12. For email, the message explicitly mentioning, and messages in the same issue discussion, we could establish sender and receiver(s). For messages without explicit receivers, particularly some IRC messages, we assume those who entered the same channel in 24 h as the receivers.

  13. The IBM Watson AlchemyLanguage API has been sunset and replaced by Tone Analyzer (https://www.ibm.com/cloud/watson-tone-analyzer). Unfortunately, Tone Analyzer stopped providing sentiment polarities.

  14. Here, we take a very conservative attitude towards labeling an event as “D.” Only the explicitly negative (sentiment polarity score \(< -0.33\)) communication records are labeled as “D.” Doing so helps us reduce the negative effect in sentiment detection (Jongeling et al. 2017; Lin et al. 2018; Wang 2019).

  15. The U.S. Court of Appeals for the Ninth Circuit had ruled that accessing and downloading public user profiles on LinkedIn does not violate the Computer Fraud and Abuse Act (CFAA), see: https://law.justia.com/cases/federal/appellate-courts/ca9/17-16783/17-16783-2019-09-09.html.

  16. We tested a wide range of the number of independent simulation runs (1000–50,000) and found the result is stable when it reaches 7000–8000.

  17. The whole sampled history space can be very large. For example, assume the whole relevant history = 30 and the sample size m = 15, there are \(C_{30}^{15} = 155,117,520\) different sampled histories. IIAG uses a simple Monte Carlo process to control the simulation.

  18. A noisy historical strategy refers to a random strategy. For example, an individual may decide to defect for not in a good mood.

  19. http://www.wired.com/2008/06/pb-theory/.

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Acknowledgements

The authors would like to thank the editors and the anonymous reviewers for their great help and insightful comments in improving this paper during the review process.

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  1. School of Software Engineering, Beijing University of Posts and Telecommunications, Beijing, China

    Yi Wang

  2. Department of Informatics, University of California, Irvine, USA

    David Redmiles

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Wang, Y., Redmiles, D. IIAG: a data-driven and theory-inspired approach for advising how to interact with new remote collaborators in OSS teams. Autom Softw Eng 28, 5 (2021). https://doi.org/10.1007/s10515-021-00283-0

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