What we have learned about software engineering expertise

Riedl, Thomas R.; Weitzenfeld, Julian; Freeman, Jared; Klein, Gary; Musa, Donald

doi:10.1007/bfb0024298

Cited by 10 publications

(5 citation statements)

References 6 publications

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“…Second, group dynamics and interactions facilitate cross fertilization and the exchange of ideas, ideally leading to a situation where the whole group performs better than the sum of its individual participants. Thus the social context has an effect on how people work [145]. Conversely, personality clashes may come into effect in groups and disrupt their progress, an effect that would not occur when developers work individually.…”

Section: • Students Vs Professionalsmentioning

confidence: 99%

Using Students as Experimental Subjects in Software Engineering Research -- A Review and Discussion of the Evidence

Feitelson

2015

Preprint

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Should students be used as experimental subjects in software engineering? Given that students are in many cases readily available and cheap it is no surprise that the vast majority of controlled experiments in software engineering use them. But they can be argued to constitute a convenience sample that may not represent the target population (typically "real" developers), especially in terms of experience and proficiency. This causes many researchers (and reviewers) to have reservations about the external validity of student-based experiments, and claim that students should not be used. Based on an extensive review of published works that have compared students to professionals, we find that picking on "students" is counterproductive for two main reasons. First, classifying experimental subjects by their status is merely a proxy for more important and meaningful classifications, such as classifying them according to their abilities, and effort should be invested in defining and using these more meaningful classifications. Second, in many cases using students is perfectly reasonable, and student subjects can be used to obtain reliable results and further the research goals. In particular, this appears to be the case when the study involves basic programming and comprehension skills, when tools or methodologies that do not require an extensive learning curve are being compared, and in the initial formative stages of large industrial research initiatives -in other words, in many of the cases that are suitable for controlled experiments of limited scope.

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Section: • Students Vs Professionalsmentioning

confidence: 99%

Using Students as Experimental Subjects in Software Engineering Research -- A Review and Discussion of the Evidence

Feitelson

2015

Preprint

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“…Moreover, they educated other team members during the working process. In a similar manner, Riedl, Weitzenfeld, Freeman, Klein, and Musa (1991) also reported high interpersonal skills in expert software engineers.…”

Section: High Performers' Involvement In Cooperation Processesmentioning

confidence: 62%

High performance and meeting participation: An observational study in software design teams.

Sonnentag¹

2001

Group Dynamics: Theory, Research, and Practice

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This study compared high and moderate performers' involvement in cooperation processes. The author used an observational method to exarnine meeting participation of 60 software professionals from 10 software projects. Analyses showed that high performers participated more in the overall meeting process. In poody structured meetings, high performers contributed more to process regulation activities, such as meeting management, goal setting, problem comprehension, and feedback seeking. No differences in process regulation activities were found for highly structured meetings. Contributions to team processes as a whole differed largely across individual team members, with high performers playing an important role and showing a high adaptation to the specific situational requirements.

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“…For example, research finds that expert programmers have superior communication and collaboration competencies (e.g., Kelley & Caplan, 1993), engage in more collaborative learning and teaching behaviors (Curtis et al, 1988), and are more likely to review code and consult with their peers (Sonnentag, 1995). When comparing top and average software developers, research showed that interpersonal networking ability adds to general cognitive ability (Kelly & Caplan, 1993;Riedl et al, 1991). Such differences between experts and others persist in OSS projects.…”

Section: Expertisementioning

confidence: 99%

Expertise and Complexity as Mediators of Knowledge Loss in Open Source Software Development

Newton

Fiore

Song

2019

Proceedings of the Human Factors and Ergonomics Society Annual

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This paper describes an approach integrating cognitive engineering with computational social science in the context of open source software (OSS) development. Through an analysis of large-scale collaborations in a complex operational setting, we study how expertise and task complexity predict changes in productivity when knowledge loss occurs. Using team data from thousands of software files, we model the effects of expertise, complexity, and knowledge loss on productivity. On its own, knowledge loss had a negative effect on productivity, but this effect was reversed when knowledge loss was combined with high complexity and high numbers of newcomers, suggesting that experts are better able to utilize crowdsourced work. We identify opportunities for research to inform prediction of outcomes in OSS projects based on team and task characteristics and demonstrate the value of integrating cognitive engineering with computational social science to study collaborative work in sociotechnical systems.

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What we have learned about software engineering expertise

Cited by 10 publications

References 6 publications

Using Students as Experimental Subjects in Software Engineering Research -- A Review and Discussion of the Evidence

Using Students as Experimental Subjects in Software Engineering Research -- A Review and Discussion of the Evidence

High performance and meeting participation: An observational study in software design teams.

Expertise and Complexity as Mediators of Knowledge Loss in Open Source Software Development

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