Top

Automated Software Engineering

Published in:

01-11-2021

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

Authors: Yi Wang, David Redmiles

Published in: Automated Software Engineering | Issue 2/2021

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

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.

previous article Prioritizing refactorings for security-critical code

next article GUI visual aspect migration: a framework agnostic solution

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

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

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.

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).

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.

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.

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.

We ignore the status differences resulting from different locations.

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

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

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

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.

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.

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.

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).

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.

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.

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.

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

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

Adams, D.A., Nelson, R.R., Todd, P.A.: Perceived Usefulness, Ease of Use, and Usage of Information Technology: A Replication, pp. 227–247. MIS Quarterly, Minneapolis (1992)

Ahmed, T., Bosu, A., Iqbal, A., Rahimi, S.: Senticr: a customized sentiment analysis tool for code review interactions. In: 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE ’17), IEEE, pp. 106–111 (2017)

Al-Ani, B., Bietz, M.J., Wang, Y., Trainer, E., Koehne, B., Marczak, S., Redmiles, D., Prikladnicki, R.: Globally distributed system developers: their trust expectations and processes. In: Proceedings of the 2013 Conference on Computer Supported Cooperative Work. ACM, New York, CSCW ’13, pp. 563–574 (2013). https://doi.org/10.1145/2441776.2441840

Alami, A., Cohn, M.L., Waisowski, A.: How do foss communities decide to accept pull requests? In: Proceedings of the Evaluation and Assessment in Software Engineering. Association for Computing Machinery, New York, EASE ’20, pp 220–229 (2020). https://doi.org/10.1145/3383219.3383242

Arthur, W.B.: Designing economic agents that act like human agents: a behavioral approach to bounded rationality. Am. Econ. Rev. 81, 353–359 (1991)

Avram, G., Bannon, L., Bowers, J., Sheehan, A., Sullivan, D.K.: Bridging, patching and keeping the work flowing: defect resolution in distributed software development. Comput. Support. Coop. Work (CSCW) 18(5–6), 477 (2009)CrossRef

Baron, R., Markman, G.: Beyond social capital: how social skills can enhance entrepreneurs’ success. Acad. Manag. Exec. 14(1), 106–116 (2000)

Bechar-Israeli, H.: From \(<\)Bonehead\(>\) to \(<\)cLoNehEAd\(>\): nicknames, play, and identity on internet relay chat1. J. Comput.-Med. Commun. 1(2), jCMC127 (1995). https://doi.org/10.1111/j.1083-6101.1995.tb00325.xCrossRef

Begel, A., Zimmermann, T.: Analyze this! 145 questions for data scientists in software engineering. In: Proceedings of the 36th International Conference on Software Engineering. ACM, New York, ICSE, pp. 12–23 (2014). https://doi.org/10.1145/2568225.2568233

Begel, A., Khoo, Y.P., Zimmermann, T.: Codebook: discovering and exploiting relationships in software repositories. In: Proceedings of the 32Nd ACM/IEEE International Conference on Software Engineering, vol. 1, pp. 125–134. ACM, New York, ICSE ’10 (2010). https://doi.org/10.1145/1806799.1806821

Bird, C., Nagappan, N., Devanbu, P., Gall, H., Murphy, B.: Does distributed development affect software quality? An empirical case study of windows vista. Commun. ACM 52(8), 85–93 (2009)CrossRef

Bjørn, P., Christensen, L.R.: Relation work: creating socio-technical connections in global engineering. In: Proceedings of the 12th European Conference on Computer Supported Cooperative Work (ECSCW’12). Springer, pp. 133–152 (2011)

Boden, A., Nett, B., Wulf, V.: Trust and social capital: revisiting an offshoring failure story of a small German software company. In: Proceedings of the 11th European Conference on Computer Supported Cooperative Work (ECSCW’09). Springer, pp. 123–142 (2009)

Bonaccio, S., Dalal, R.S.: Advice taking and decision-making: an integrative literature review, and implications for the organizational sciences. Organ. Behav. Hum. Decis. Process. 101(2), 127–151 (2006)CrossRef

Borgatti, S.P., Foster, P.C.: The network paradigm in organizational research: a review and typology. J. Manag. 29(6), 991–1013 (2003)

Boyd, D., Crawford, K.: Critical questions for big data: provocations for a cultural, technological, and scholarly phenomenon. Inform., Commun. Soc. 15(5), 662–679 (2012)CrossRef

Calefato, F., Lanubile, F.: Socialcde: a social awareness tool for global software teams. In: Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering. ACM, New York, ESEC/FSE 2013, pp. 587–590 (2013). https://doi.org/10.1145/2491411.2494592

Calefato, F., Lanubile, F.: Affective trust as a predictor of successful collaboration in distributed software projects. In: 2016 IEEE/ACM 1st International Workshop on Emotional Awareness in Software Engineering (SEmotion). IEEE, pp. 3–5 (2016)

Calefato, F., Lanubile, F., Novielli N.: A preliminary analysis on the effects of propensity to trust in distributed software development. In: 2017 IEEE 12th International Conference on Global Software Engineering (ICGSE ’17). IEEE, pp. 56–60 (2017)

Calefato, F., Lanubile, F., Maiorano, F., Novielli, N.: Sentiment polarity detection for software development. Empir. Softw. Eng. 23(3), 1352–1382 (2018)CrossRef

Carmel, E., Agarwal, R.: Tactical approaches for alleviating distance in global software development. IEEE Softw. 18(2), 22–29 (2001)CrossRef

Carmines, E.G., Zeller, R.A.: Reliability and Validity Assessment, vol. 17. Sage Publications, New York (1979)CrossRef

Castelle, M.: The linguistic ideologies of deep abusive language classification. In: Proceedings of the 2nd Workshop on Abusive Language Online (ALW2), pp. 160–170 (2018)

Cataldo, M., Herbsleb, J.D., Carley, K.M.: Socio-technical congruence: a framework for assessing the impact of technical and work dependencies on software development productivity. In: Proceedings of the Second ACM-IEEE International Symposium on Empirical Software Engineering and Measurement. ACM, New York, ESEM ’08, pp. 2–11 (2008). https://doi.org/10.1145/1414004.1414008

Chowdhury, S.A., Hindle, A.: Mining stackoverflow to filter out off-topic irc discussion. In: Proceedings of the 12th Working Conference on Mining Software Repositories. IEEE Press, Piscataway, MSR ’15, pp. 422–425 (2015)

Chowdhury, S.K., Wynn, J.: Cowabunga!: a system to facilitate multi-cultural diversity through couchsurfing. In: CHI ’11 Extended Abstracts on Human Factors in Computing Systems. ACM, New York, CHI EA ’11, pp. 965–970 (2011). https://doi.org/10.1145/1979742.1979506

Connelly, B.L., Certo, S.T., Ireland, R.D., Reutzel, C.R.: Signaling theory: a review and assessment. J. Manag. 37(1), 39–67 (2011)

Cooke, N.J., Hilton, M.L.: Enhancing the Effectiveness of Team Science. National Academies Press, Washington, D.C. (2015)

Cooper, R.: Coordination Games. Cambridge University Press, CambridgeCambridge (1999)MATHCrossRef

Davis, F.D.: Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology, pp. 319–340. MIS Quarterly, Minneapolis (1989)

Dourish, P., Bly, S.: Portholes: supporting awareness in a distributed work group. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, New York, CHI ’92, pp. 541–547 (1992). https://doi.org/10.1145/142750.142982

Drogendijk, R., Slangen, A.: Hofstede, Schwartz, or managerial perceptions? the effects of different cultural distance measures on establishment mode choices by multinational enterprises. Int. Bus. Rev. 15(4), 361–380 (2006)CrossRef

Easley, D., Kleinberg, J.: Networks, Crowds, and Markets: Reasoning About a Highly Connected World. Cambridge University Press, New York (2010)MATHCrossRef

Erev, I., Roth, A.E.: Predicting how people play games: reinforcement learning in experimental games with unique, mixed strategy equilibria. Am. Econ. Rev. 88(4), 848–881 (1998)

Erickson, T., Kellogg, W.A.: Social translucence: an approach to designing systems that support social processes. ACM Trans. Comput.-Hum. Interact. 7(1), 59–83 (2000). https://doi.org/10.1145/344949.345004CrossRef

Erickson, T., Smith, D.N., Kellogg, W.A., Laff, M., Richards, J.T., Bradner, E.: Socially translucent systems: social proxies, persistent conversation, and the design of & ldquo;babble & rdquo;. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, New York, CHI ’99, pp. 72–79 (1999). https://doi.org/10.1145/302979.302997

Espinosa, J.A., Nan, N., Carmel, E.: Temporal distance, communication patterns, and task performance in teams. J. Manag. Inform. Syst. 32(1), 151–191 (2015)CrossRef

Fang, F., Nguyen, T.H., Pickles, R., Lam, W.Y., Clements, G.R., An, B., Singh, A., Schwedock, B.C., Tambe, M., Lemieux, A.: Paws-a deployed game-theoretic application to combat poaching. AI Mag. 38(1), 23–36 (2017)

Fiske, S.T., Cuddy, A.J., Glick, P., Xu, J.: A model of (often mixed) stereotype content: competence and warmth respectively follow from perceived status and competition. J. Pers. Soc. Psychol. 82(6), 878–902 (2002)CrossRef

Fulmer, C.A., Gelfand, M.J.: At what level (and in whom) we trust trust across multiple organizational levels. J. Manag. 38(4), 1167–1230 (2012)

Fussell, S.R., Kraut, R.E., Lerch, F.J., Scherlis, W.L., McNally, M.M., Cadiz, J.J.: Coordination, overload and team performance: effects of team communication strategies. In: Proceedings of the 1998 ACM Conference on Computer Supported Cooperative Work. ACM, New York, CSCW ’98, pp. 275–284 (1998). https://doi.org/10.1145/289444.289502

Gawronski, B., Payne, B.K.: Handbook of Implicit Social Cognition: Measurement, Theory, and Applications. Guilford Press, New York (2011)

Gintis, H.: The Bounds of Reason: Game Theory and the Unification of the Behavioral Sciences. Princeton University Press, Princeton (2009)MATH

Gregor, S.: The Nature of Theory in Information Systems, pp. 611–642. MIS Quarterly, Minneapolis (2006)

Groysberg, B., Polzer, J.T., Elfenbein, H.A.: Too many cooks spoil the broth: how high-status individuals decrease group effectiveness. Organ. Sci. 22(3), 722–737 (2011)CrossRef

Gutwin, C., Penner, R., Schneider, K.: Group awareness in distributed software development. In: Proceedings of the 2004 ACM Conference on Computer Supported Cooperative Work. ACM, New York, CSCW ’04, pp. 72–81 (2004). https://doi.org/10.1145/1031607.1031621

Hall, T., Sharp, H., Beecham, S., Baddoo, N., Robinson, H.: What do we know about developer motivation? IEEE Softw. 25(4), 92–94 (2008)CrossRef

Hanus, M.D., Fox, J.: Persuasive avatars: the effects of customizing a virtual salesperson’s appearance on brand liking and purchase intentions. Int. J. Hum. Comput. Stud. 84, 33–40 (2015)CrossRef

Harsanyi, J.C.: Rule utilitarianism and decision theory. Erkenntnis 11(1), 25–53 (1977)CrossRef

Harsanyi, J.C.: Rule utilitarianism, rights, obligations and the theory of rational behavior. In: Papers in Game Theory. Springer, pp. 235–253 (1982)

Hendry, D.G.: Public participation in proprietary software development through user roles and discourse. Int. J. Hum. Comput. Stud. 66(7), 545–557 (2008)CrossRef

Herbsleb, J.D.: Global software engineering: the future of socio-technical coordination. In: 2007 Future of Software Engineering. IEEE Computer Society, Washington, DC, FOSE ’07, pp. 188–198 (2007). https://doi.org/10.1109/FOSE.2007.11

Herbsleb, J.D., Grinter, R.E.: Architectures, coordination, and distance: Conway’s law and beyond. IEEE Softw. 16(5), 63–70 (1999)CrossRef

Hincapié-Ramos, J.D., Voida, S., Mark, G.: Sharing availability information with interruptme. In: Proceedings of the 13th International Conference on Ubiquitous Computing. ACM, New York, UbiComp ’11, pp. 477–478 (2011). https://doi.org/10.1145/2030112.2030179

Hinds, P., Liu, L., Lyon, J.: Putting the global in global work: an intercultural lens on the practice of cross-national collaboration. Acad. Manag. Ann. 5(1), 135–188 (2011)CrossRef

Hinds, P.J., Mortensen, M.: Understanding conflict in geographically distributed teams: the moderating effects of shared identity, shared context, and spontaneous communication. Organ. Sci. 16(3), 290–307 (2005)CrossRef

Hofstede, G., Bond, M.H.: Hofstede’s culture dimensions: an independent validation using Rokeach’s value survey. J. Cross Cult. Psychol. 15(4), 417–433 (1984)CrossRef

Hornsey, M.J., Hogg, M.A.: Subgroup relations: a comparison of mutual intergroup differentiation and common ingroup identity models of prejudice reduction. Pers. Soc. Psychol. Bull. 26(2), 242–256 (2000)CrossRef

Islam, M.R., Zibran, M.F.: Leveraging automated sentiment analysis in software engineering. In: 2017 IEEE/ACM 14th International Conference on Mining Software Repositories (MSR ’17). IEEE, pp. 203–214 (2017)

Jarvenpaa, S.L., Leidner, D.E.: Communication and trust in global virtual teams. Organ. Sci. 10(6), 791–815 (1999)CrossRef

Jarvenpaa, S.L., Shaw, T.R., Staples, D.S.: Toward contextualized theories of trust: the role of trust in global virtual teams. Inform. Syst. Res. 15(3), 250–267 (2004)CrossRef

Jongeling, R., Sarkar, P., Datta, S., Serebrenik, A.: On negative results when using sentiment analysis tools for software engineering research. Empir. Softw. Eng 22(5), 2543–2584 (2017)CrossRef

Kahneman, D., Tversky, A.: Choices, Values, and Frames. Cambridge University Press, New York (2000)MATHCrossRef

Kiritchenko, S., Zhu, X., Mohammad, S.M.: Sentiment analysis of short informal texts. J. Artif. Intell. Res. 50, 723–762 (2014)CrossRef

Kwan, I., Schröter, A., Damian, D.: Does socio-technical congruence have an effect on software build success? A study of coordination in a software project. IEEE Trans. Softw. Eng. 37(3), 307–324 (2011)CrossRef

Largent, E.A., Lynch, H.F.: Paying research participants: regulatory uncertainty, conceptual confusion, and a path forward. Yale J. Health Policy, Law, Ethics 17(1), 61–141 (2017)

Laumer, S., Shami, N.S., Muller, M., Geyer, W.: The challenge of enterprise social networking (non-) use at work: a case study of how to positively influence employees’ enterprise social networking acceptanc. In: Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. ACM, New York, CSCW ’17, pp. 978–994 (2017). https://doi.org/10.1145/2998181.2998309

Leonardi, P.M., Huysman, M., Steinfield, C.: Enterprise social media: definition, history, and prospects for the study of social technologies in organizations. J. Comput.-Med. Commun. 19(1), 1–19 (2013)

Levina, N., Vaast, E.: Innovating or Doing as Told? Status Differences and Overlapping Boundaries in Offshore Collaboration, pp. 307–332. MIS Quarterly, Minneapolis (2008)

Lin, B., Zampetti, F., Bavota, G., Di Penta, M., Lanza, M., Oliveto, R.: Sentiment analysis for software engineering: How far can we go? In: Proceedings of the 40th International Conference on Software Engineering. Association for Computing Machinery, New York, ICSE ’18, pp. 94–104 (2018). https://doi.org/10.1145/3180155.3180195

Lopez, G., Guerrero, L.A.: Awareness supporting technologies used in collaborative systems: a systematic literature review. In: Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. ACM, New York, CSCW ’17, pp. 808–820 (2017). https://doi.org/10.1145/2998181.2998281

Lycett, M.: ‘Datafication’: making sense of (big) data in a complex world. Eur. J. Inform. Syst. 22(4), 381–386 (2013)CrossRef

Marlow, J., Dabbish, L., Herbsleb, J.: Impression formation in online peer production: activity traces and personal profiles in github. In: Proceedings of the 2013 Conference on Computer Supported Cooperative Work. ACM, New York, CSCW ’13, pp. 117–128 (2013). https://doi.org/10.1145/2441776.2441792

Maznevski, M.L., Chudoba, K.M.: Bridging space over time: global virtual team dynamics and effectiveness. Organ. Sci. 11(5), 473–492 (2000)CrossRef

McKnight, D.H., Cummings, L.L., Chervany, N.L.: Initial trust formation in new organizational relationships. Acad. Manag. Rev. 23(3), 473–490 (1998)CrossRef

Menzies, T.: Beyond data mining; towards “idea engineering”. In: Proceedings of the 9th International Conference on Predictive Models in Software Engineering. ACM, New York, PROMISE ’13, pp. 11:1–11:6 (2013). https://doi.org/10.1145/2499393.2499401

Menzies, T., Zimmermann, T.: Goldfish bowl panel: software development analytics. In: Proceedings of the 34th International Conference on Software Engineering. IEEE Press, Piscataway, ICSE ’12, pp. 1032–1033 (2012)

Menzies, T., Milton, Z., Turhan, B., Cukic, B., Jiang, Y., Bener, A.: Defect prediction from static code features: current results, limitations, new approaches. Autom. Softw. Eng 17(4), 375–407 (2010)CrossRef

Monroe, B.L., Pan, J., Roberts, M.E., Sen, M., Sinclair, B.: No! formal theory, causal inference, and big data are not contradictory trends in political science. PS: Polit. Sci. Polit. 48(1), 71–74 (2015)

Murphy-Hill, E., Zimmermann, T., Nagappan, N.: Cowboys, ankle sprains, and keepers of quality: how is video game development different from software development? In: Proceedings of the 36th International Conference on Software Engineering. ACM, New York, ICSE 2014, pp. 1–11 (2014). https://doi.org/10.1145/2568225.2568226

Myerson, R.B.: Refinements of the nash equilibrium concept. Int. J. Game Theory 7(2), 73–80 (1978)MathSciNetMATHCrossRef

Nakakoji, K., Yamamoto, Y., Nishinaka, Y., Kishida, K., Ye, Y.: Evolution patterns of open-source software systems and communities. In: Proceedings of the International Workshop on Principles of Software Evolution. Association for Computing Machinery, New York, IWPSE ’02, pp. 76–85 (2002). https://doi.org/10.1145/512035.512055

North, M.J., Macal, C.M.: Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling and Simulation. Oxford University Press, Oxford (2007)CrossRef

Novielli, N., Girardi, D., Lanubile, F.: A benchmark study on sentiment analysis for software engineering research. In: Proceedings of the 2018 IEEE/ACM 15th International Conference on Mining Software Repositories (MSR ’18). IEEE, pp. 364–375 (2018)

O’Leary, M., Cummings, J.: The spatial, temporal, and configurational characteristics of geographic dispersion in teams. MIS Quart. 31(3), 433–452 (2007)CrossRef

Olson, G.M., Olson, J.S.: Distance matters. Hum.-Comput. Int. 15(2), 139–178 (2000)CrossRef

Paikari, E., Ruhe, G., Southekel, P.H.: Simulation-based decision support for bringing a project back on track: the case of rup-based software construction. In: 2012 International Conference on Software and System Process (ICSSP ’12). IEEE, pp. 13–22 (2012)

Paunonen, S.V.: Hierarchical organization of personality and prediction of behavior. J. Pers. Soc. Psychol. 74(2), 538 (1998)CrossRef

Powell, W.W., White, D.R., Koput, K.W., Owen-Smith, J.: Network dynamics and field evolution: the growth of interorganizational collaboration in the life sciences. Am. J. Sociol. 110(4), 1132–1205 (2005)CrossRef

Power, D.J.: Understanding data-driven decision support systems. Inform. Syst. Manag. 25(2), 149–154 (2008)CrossRef

Reagans, R., Zuckerman, E., McEvily, B.: How to make the team: social networks vs. demography as criteria for designing effective teams. Adm. Sci. Quart. 49(1), 101–133 (2004)CrossRef

Riegelsberger, J., Sasse, M.A., McCarthy, J.D.: The researcher’s dilemma: evaluating trust in computer-mediated communication. Int. J. Hum Comput Stud. 58(6), 759–781 (2003)CrossRef

Rubinstein, R.Y., Kroese, D.P.: Simulation and the Monte Carlo Method, vol. 10. Wiley, Hoboken (2016)MATHCrossRef

Runeson, P., Höst, M.: Guidelines for conducting and reporting case study research in software engineering. Empir. Softw. Eng 14(2), 131 (2009)CrossRef

Sapkota, H., Murukannaiah, P.K., Wang, Y.: A network-centric approach for estimating trust between open source software developers. PLoS ONE 14(12), e0226,281 (2019)CrossRef

Sarker, S., Ahuja, M., Sarker, S., Kirkeby, S.: The role of communication and trust in global virtual teams: a social network perspective. J. Manag. Inform. Syst. 28(1), 273–310 (2011)CrossRef

Sarma, A., Redmiles, D., Avd, H.: Categorizing the spectrum of coordination technology. Computer 43(6), 61–67 (2010). https://doi.org/10.1109/MC.2010.163CrossRef

Schumann, J., Shih, P.C., Redmiles, D.F., Horton, G.: Supporting initial trust in distributed idea generation and idea evaluation. In: Proceedings of the 17th ACM International Conference on Supporting Group Work. ACM, New York, GROUP ’12, pp. 199–208 (2012). https://doi.org/10.1145/2389176.2389207

Seering, J., Ng, F., Yao, Z., Kaufman, G.: Applications of social identity theory to research and design in computer-supported cooperative work. In: Proc. ACM Hum.-Comput. Interact 2 (CSCW), pp. :201:1–201:34 (2018). https://doi.org/10.1145/3274771

Shim, J.P., Warkentin, M., Courtney, J.F., Power, D.J., Sharda, R., Carlsson, C.: Past, present, and future of decision support technology. Decis. Support Syst. 33(2), 111–126 (2002)CrossRef

Silva, J.O., Wiese, I.S., Steinmacher, I., Gerosa, M.A.: Students’ engagement in open source projects: an analysis of google summer of code. In: Proceedings of the 31st Brazilian Symposium on Software Engineering. Association for Computing Machinery, New York, SBES ’17, pp. 224–233 (2017). https://doi.org/10.1145/3131151.3131156

Simon, H.A.: Bounded rationality and organizational learning. Organ. Sci. 2(1), 125–134 (1991)CrossRef

Skyrms, B.: The stag hunt. In: Proceedings and Addresses of the American Philosophical Association, pp. 31–41 (2001)

Skyrms, B.: The Stag Hunt and the Evolution of Social Structure. Cambridge University Press, New York (2004)

Skyrms, B.: Trust, risk, and the social contract. Synthese 160(1), 21–25 (2008)MathSciNetCrossRef

Steinmacher, I., Chaves, A.P., Gerosa, M.: Awareness support in distributed software development: a systematic review and mapping of the literature. Comput. Support. Coop. Work (CSCW) 22(2–3), 113–158 (2013)CrossRef

Steinmacher, I., Conte, T.U., Treude, C., Gerosa, M.A.: Overcoming open source project entry barriers with a portal for newcomers. In: Proceedings of the 38th International Conference on Software Engineering. ACM, New York, ICSE ’16, pp. 273–284 (2016). https://doi.org/10.1145/2884781.2884806

Steinmacher, I., Gerosa, M., Conte, T.U., Redmiles, D.F.: Overcoming social barriers when contributing to open source software projects. Comput. Support. Coop. Work (CSCW) 28(1), 247–290 (2019). https://doi.org/10.1007/s10606-018-9335-zCrossRef

Storey, M.A., Zagalsky, A.: Disrupting developer productivity one bot at a time. In: Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering. ACM, New York, FSE 2016, pp. 928–931 (2016). https://doi.org/10.1145/2950290.2983989

Storey, M.A., Treude, C., van Deursen, A., Cheng, L.T.: The impact of social media on software engineering practices and tools. In: Proceedings of the FSE/SDP Workshop on Future of Software Engineering Research. ACM, New York, FoSER ’10, pp. 359–364 (2010). https://doi.org/10.1145/1882362.1882435

Tamburri, D.A., Kazman, R., Fahimi, H.: The architect’s role in community shepherding. IEEE Softw. 33(6), 70–79 (2016). https://doi.org/10.1109/MS.2016.144CrossRef

Tang, J.C., Yankelovich, N., Begole, J., Van Kleek, M., Li, F., Bhalodia, J.: Connexus to awarenex: Extending awareness to mobile users. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, New York, CHI ’01, pp. 221–228 (2001). https://doi.org/10.1145/365024.365105

Tourani, P., Adams, B., Serebrenik, A.: Code of conduct in open source projects. In: 2017 IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER’17). IEEE, pp. 24–33 (2017)

Train, K.E.: Discrete Choice Methods with Simulation. Cambridge University Press, New York (2009)MATH

Trainer, E.H., Redmiles, D.F.: Bridging the gap between awareness and trust in globally distributed software teams. J. Syst. Softw. 144, 328–341 (2018)CrossRef

Turban, E., Sharda, R., Delen, D.: Decision Support and Business Intelligence Systems, 9th edn. Prentice Hall Press, Upper Saddle River (2010)

van Wesel, P., Lin, B., Robles, G., Serebrenik, A.: Reviewing career paths of the openstack developers. In: 2017 IEEE International Conference on Software Maintenance and Evolution (ICSME), pp. 544–548 (2017). https://doi.org/10.1109/ICSME.2017.25

Von Neumann, J., Morgenstern, O.: Theory of Games and Economic Behavior (Commemorative Edition). Princeton University Press, Princeton (2007)

Walker, R., Holmes, R.: Simulation. In: Robillard, M.P., Maalej, W., Walker, R.J., Zimmermann, T. (eds.) Recommendation Systems in Software Engineering, pp. 301–327. Springer, New York (2014)CrossRef

Wang, Y.: Emotions extracted from text vs. true emotions—an empirical evaluation in se context. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE). IEEE, pp. 230–242 (2019)

Wang, Y., Redmiles, D.: Cheap talk, cooperation, and trust in global software engineering. Empir. Softw. Eng. 21(6), 2233–2267 (2016a). https://doi.org/10.1007/s10664-015-9407-3CrossRef

Wang, Y., Redmiles, D.: The diffusion of trust and cooperation in teams with individuals’ variations on baseline trust. In: Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing. ACM, New York, CSCW ’16, pp. 303–318 (2016b). https://doi.org/10.1145/2818048.2820064

Wang, Y., Redmiles, D.: Exploring trust and cooperation development with agent-based simulation in a pseudo scale-free network. In: Proceedings of the 19th International Conference on Supporting Group Work. ACM, New York, GROUP ’16, pp 121–130 (2016c). https://doi.org/10.1145/2957276.2957305

Wang, Y., Wang, Z., Redmiles, D.: The co-evolution of trust and coordination in global software development teams-an extensible evolutionary game theory model. In: Proceedings of the 52nd Hawaii International Conference on System Sciences, p. 572 (2019)

Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP ’05), pp. 347–354 (2005)

Wu, A., DiMicco, J.M., Millen, D.R.: Detecting professional versus personal closeness using an enterprise social network site. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, New York, CHI ’10, pp. 1955–1964 (2010). https://doi.org/10.1145/1753326.1753622

Yang, Y., Karim, M.R., Saremi, R., Ruhe, G.: Who should take this task? dynamic decision support for crowd workers. In: Proceedings of the 10th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement. Association for Computing Machinery, New York, ESEM ’16 (2016). https://doi.org/10.1145/2961111.2962594

Young, H.P.: Individual Strategy and Social Structure: An Evolutionary Theory of Institutions. Princeton University Press, Princeton (2001)

Title: IIAG: a data-driven and theory-inspired approach for advising how to interact with new remote collaborators in OSS teams
Authors: Yi Wang
David Redmiles
Publication date: 01-11-2021
Publisher: Springer US
Published in: Automated Software Engineering / Issue 2/2021
Print ISSN: 0928-8910
Electronic ISSN: 1573-7535
DOI: https://doi.org/10.1007/s10515-021-00283-0

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 2/2021

GUI visual aspect migration: a framework agnostic solution

SSD based waste separation in smart garbage using augmented clustering NMS

Information security implications of using NLP in IT outsourcing: a Diffusion of Innovation theory perspective

Natural language ambiguity resolution by intelligent semantic annotation of software requirements

Traceability recovery between bug reports and test cases-a Mozilla Firefox case study

New failure rate model for iterative software development life cycle process

Premium Partner