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Andrew J. Ko

Because software engineering often times distributes work across multiple people, a fundamental challenge in software engineering is ensuring that everyone on a team has the same understanding of what is being built and why. In the seminal book “The Mythical Man Month”, Fred Brooks argued that good software needs to have conceptual integrity, both in how it is designed, but also how it is implemented (Brooks 1995). This is the idea that whatever vision of what is being built must stay intact, even as the building of it gets distributed to multiple people. When multiple people are responsible for implementing a single coherent idea, how can they ensure they all build the same idea?

The solution is effective communication. When communication is poor and teams are disconnected, software defects are the result (Bettenburg & Hassan 2013). The social relationships in a team also play a large role in structuring how projects evolve (Zhou & Mockus 2011). Perhaps the most notable theory underlying these ideas is Conway's Law (Conway 1968), which argues that any designed system—software included—will reflect the social relationships behind its design. For example, look at any online banking website: the way the application is designed, how information is organized, the terminology that is used, and even the visual design is a reflection of how the teams inside that bank are organized and socially connected. Put simply, Conway observed that the architectural organization of the product resembled the organizational hierarchy of the teams that produced it.

Because communication is so central, software engineers are constantly seeking information to further their work, going to their coworkers' desks, emailing them, chatting via messaging platforms, and even using social media (Ko et al. 2007). Some of the information that developers are seeking is easier to find than others. For example, in the study I just cited, it was pretty trivial to find information about how wrote a line of code or whether a build was done, but when the information they needed resided in someone else's head (e.g., why a particular line of code was written), it was slow or often impossible to retrieve it. Sometimes it's not even possible to find out who has the information. Researchers have investigated tools for trying to quantify expertise by automatically analyzing the code that developers have written, building platforms to help developers search for other developers who might know what they need to know (Mockus & Herbsleb 2002, Begel et al. 2010).

Communication is not always effective. In fact, there are many kinds of communication that are highly problematic in software engineering teams. For example, Perlow (1999) conducted an ethnography of one team and found a highly dysfunctional use of interruptions in which the most expert members of a team were constantly interrupted to “fight fires” (immediately address critical problems) in other parts of the organization, and then the organization rewarded them for their heroics. This not only made the most expert engineers less productive, but it also disincentivized the rest of the organization to find effective ways of preventing the disasters from occurring in the first place. Not all interruptions are bad, and they can increase productivity, but they do increase stress (Mark et al. 2008).

Communication isn't just about transmitting information; it's also about relationships and identity. For example, the dominant culture of many software engineering work environments—and even the perceived culture—is one that can deter many people from even pursuing careers in computer science. Modern work environments are still dominated by men, who speak loudly, out of turn, and disrespectfully, with some even bordering on sexual harassment. Similarly, software developers often have to work with people in other domains such as artists, content developers, data scientists, design researchers, designers, electrical engineers, mechanical engineers, product planners, program managers, and service engineers. One study found that developers' cross-disciplinary collaborations with people in these other domains required open-mindedness about the input of others, proactively informing everyone about code-related constraints, and ultimately seeing the broader picture of how pieces from different disciplines fit together; when developers didn't do these things, collaborations failed, and therefore projects failed (Li et al. 2017). These are not the conditions for trusting, effective communication.

When communication is effective, it still takes time. One of the key strategies for reducing the amount of communication necessary is knowledge sharing tools, which broadly refers to any information system that stores facts that developers would normally have to retrieve from a person. By storing them in a database and making them easy to search, teams can avoid interruptions. The most common knowledge sharing tools in software teams are issue trackers, which are often at the center of communication not only between developers, but also with every other part of a software organization (Bertram et al. 2010). Community portals, such as GitHub pages or Slack teams, can also be effective ways of sharing documents and archiving decisions (Treude & Storey 2011). Perhaps the most popular knowledge sharing tool in software engineering today is Stack Overflow, which archives facts about programming language and API usage.

Because all of this knowledge is so critical to progress, when developers leave an organization and haven't archived their knowledge somewhere, it can be quite disruptive to progress. Organizations often have single points of failure, in which a single developer may be critical to a team's ability to maintain and enhance a software product (Rigby et al. 2016). When newcomers join a team and lack the right knowledge, they introduce defects (Foucault et al. 2015). Some companies try to mitigate this by rotating developers between projects, “cross-training” them to ensure that the necessary knowledge to maintain a project is distributed across multiple engineers.

What does all of this mean for you as an individual developer? To put it simply, don't underestimate the importance of talking. Know who you need to talk to, talk to them frequently, and to the extent that you can, write down what you know both to lessen the demand for talking and mitigate the risk of you not being available, but also to make your knowledge more precise and accessible in the future. It often takes decades for engineers to excel at communication. The very fact that you know why communication is important gives you an critical head start.

Next chapter: Productivity

Further reading

Salah Bendifallah and Walt Scacchi. 1989. Work structures and shifts: an empirical analysis of software specification teamwork. In Proceedings of the 11th international conference on Software engineering (ICSE '89). ACM, New York, NY, USA, 260-270.

Andrew Begel, Yit Phang Khoo, and Thomas Zimmermann. 2010. Codebook: discovering and exploiting relationships in software repositories. In Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 1 (ICSE '10), Vol. 1. ACM, New York, NY, USA, 125-134.

Dane Bertram, Amy Voida, Saul Greenberg, and Robert Walker. 2010. Communication, collaboration, and bugs: the social nature of issue tracking in small, collocated teams. In Proceedings of the 2010 ACM conference on Computer supported cooperative work (CSCW '10). ACM, New York, NY, USA, 291-300.

Bettenburg, N., & Hassan, A. E. (2013). Studying the impact of social interactions on software quality. Empirical Software Engineering, 18(2), 375-431.

Brooks, F.B. (1995). The Mythical Man-Month: Essays on Software Engineering, Addison-Wesley.

Conway, M. E. (1968). How do committees invent. Datamation, 14(4), 28-31.

Torgeir Dingsøyr and Emil Røyrvik. 2003. An empirical study of an informal knowledge repository in a medium-sized software consulting company. In Proceedings of the 25th International Conference on Software Engineering (ICSE '03). IEEE Computer Society, Washington, DC, USA, 84-92.

Matthieu Foucault, Marc Palyart, Xavier Blanc, Gail C. Murphy, and Jean-Rémy Falleri. 2015. Impact of developer turnover on quality in open-source software. In Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering (ESEC/FSE 2015). ACM, New York, NY, USA, 829-841.

Andrew J. Ko, Robert DeLine, and Gina Venolia. 2007. Information Needs in Collocated Software Development Teams. In Proceedings of the 29th international conference on Software Engineering (ICSE '07). IEEE Computer Society, Washington, DC, USA, 344-353.

Li, P. L., Ko, A. J., & Begel, A. (2017, May). Cross-disciplinary perspectives on collaborations with software engineers. In Proceedings of the 10th International Workshop on Cooperative and Human Aspects of Software Engineering (pp. 2-8).

Mark, G., Gudith, D., & Klocke, U. (2008, April). The cost of interrupted work: more speed and stress. In Proceedings of the SIGCHI conference on Human Factors in Computing Systems (pp. 107-110).

Audris Mockus. 2010. Organizational volatility and its effects on software defects. In Proceedings of the eighteenth ACM SIGSOFT international symposium on Foundations of software engineering (FSE '10). ACM, New York, NY, USA, 117-126.

Audris Mockus and James D. Herbsleb. 2002. Expertise browser: a quantitative approach to identifying expertise. In Proceedings of the 24th International Conference on Software Engineering (ICSE '02). ACM, New York, NY, USA, 503-512.

Perlow, L. A. (1999). The time famine: Toward a sociology of work time. Administrative science quarterly, 44(1), 57-81.

Pikkarainen, M., Haikara, J., Salo, O., Abrahamsson, P., & Still, J. (2008). The impact of agile practices on communication in software development. Empirical Software Engineering, 13(3), 303-337.

Peter C. Rigby, Yue Cai Zhu, Samuel M. Donadelli, and Audris Mockus. 2016. Quantifying and mitigating turnover-induced knowledge loss: case studies of chrome and a project at Avaya. In Proceedings of the 38th International Conference on Software Engineering (ICSE '16). ACM, New York, NY, USA, 1006-1016.

Ronnie E. S. Santos, Fabio Q. B. da Silva, Cleyton V. C. de Magalhães, and Cleviton V. F. Monteiro. 2016. Building a theory of job rotation in software engineering from an instrumental case study. In Proceedings of the 38th International Conference on Software Engineering (ICSE '16). ACM, New York, NY, USA, 971-981.

Sfetsos, P., Stamelos, I., Angelis, L., & Deligiannis, I. (2009). An experimental investigation of personality types impact on pair effectiveness in pair programming. Empirical Software Engineering, 14(2), 187.

Christoph Treude and Margaret-Anne Storey. 2011. Effective communication of software development knowledge through community portals. In Proceedings of the 19th ACM SIGSOFT symposium and the 13th European conference on Foundations of software engineering (ESEC/FSE '11). ACM, New York, NY, USA, 91-101.

Christoph Treude and Margaret-Anne Storey. 2009. How tagging helps bridge the gap between social and technical aspects in software development. In Proceedings of the 31st International Conference on Software Engineering (ICSE '09). IEEE Computer Society, Washington, DC, USA, 12-22.

Keiji Uemura and Miki Ohori. 1984. A cooperative approach to software development by application engineers and software engineers. In Proceedings of the 7th international conference on Software engineering (ICSE '84). IEEE Press, Piscataway, NJ, USA, 86-96.

Minghui Zhou and Audris Mockus. 2011. Does the initial environment impact the future of developers? In Proceedings of the 33rd International Conference on Software Engineering (ICSE '11). ACM, New York, NY, USA, 271-280.


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