Effects of Personality on Pair Programming

• Published in: IEEE transactions on software engineering 2010-01, Vol.36 (1), p.61-80
• Main Authors: Hannay, J.E., Arisholm, E., Engvik, H., Sjoberg, D.I.K.
• Format: Article
• Language: English
• Subjects: Big Five; Career counseling; Collaboration; Collaborative work; Complexity theory; Computer programming; Computer programs; Consultants; Digital Object Identifier; Employee welfare; Engineering profession; expertise; Informatics; Instruments; Job performance; Keyboards; Learning; Mathematical models; Meta-analysis; Pair programming; Personality; Personality tests; Personality traits; Professionals; Programmers; Programming; Programming profession; Psychological tests; Recruitment; Software; Software engineering; Software performance; Software testing; Studies; Task complexity
• ISSN: 0098-5589; 1939-3520
• DOI: 10.1109/TSE.2009.41

Personality tests in various guises are commonly used in recruitment and career counseling industries. Such tests have also been considered as instruments for predicting the job performance of software professionals both individually and in teams. However, research suggests that other human-related factors such as motivation, general mental ability, expertise, and task complexity also affect the performance in general. This paper reports on a study of the impact of the Big Five personality traits on the performance of pair programmers together with the impact of expertise and task complexity. The study involved 196 software professionals in three countries forming 98 pairs. The analysis consisted of a confirmatory part and an exploratory part. The results show that: (1) Our data do not confirm a meta-analysis-based model of the impact of certain personality traits on performance and (2) personality traits, in general, have modest predictive value on pair programming performance compared with expertise, task complexity, and country. We conclude that more effort should be spent on investigating other performance-related predictors such as expertise, and task complexity, as well as other promising predictors, such as programming skill and learning. We also conclude that effort should be spent on elaborating on the effects of personality on various measures of collaboration, which, in turn, may be used to predict and influence performance. Insights into such malleable, rather than static, factors may then be used to improve pair programming performance.