A machine-based personality oriented team recommender for software development organizations
Yilmaz, Murat and Al-Taei, Ali and O'Connor, Rory (2015) A machine-based personality oriented team recommender for software development organizations. In: 22nd European Conference on Systems, Software and Services Process Improvement (EuroSPI 2015), 30 Sept - 2 Oct 2015, Ankara, Turkey. ISBN 978-3-319-24647-5
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Hiring the right person for the right job is always a challenging task in software development landscapes. To bridge this gap, software_rms start using psychometric instruments for investigating the personality types of software practitioners. In our previous research, we have developed an MBTI-like instrument to reveal the personality types ofsoftware practitioners. This study aims to develop a personality-based team recommender mechanism to improve the e_ectiveness of software teams. The mechanism is based on predicting the possible patterns of teams using a machine-based classi_er. The classi_er is trained with em-pirical data (e.g. personality types, job roles), which was collected from52 software practitioners working on _ve different software teams. 12software practitioners were selected for the testing process who were recommended by the classi_er to work for these teams. The preliminary results suggest that a personality-based team recommender system mayprovide an effective approach as compared with ad-hoc methods of teamformation in software development organizations. Ultimately, the overallperformance of the proposed classi_er was 83.3%. These _ndings seemacceptable especially for tasks of suggestion where individuals might beable to _t in more than one team.
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