TY - GEN
T1 - Does personality influence the usage of static analysis tools? An explorative experiment
AU - Ostberg, Jan Peter
AU - Wagner, Stefan
AU - Weilemann, Erica
N1 - Publisher Copyright:
© 2016 ACM.
PY - 2016/5/14
Y1 - 2016/5/14
N2 - There are many techniques to improve software quality. One is using automatic static analysis tools. We have observed, however, that despite the low-cost help they offer, these tools are underused and often discourage beginners. There is evidence that personality traits influence the perceived usability of a software. Thus, to support beginners better, we need to understand how the workflow of people with different prevalent personality traits using these tools varies. For this purpose, we observed users' solution strategies and correlated them with their prevalent personality traits in an exploratory study with student participants within a controlled experiment. We gathered data by screen capturing and chat protocols as well as a Big Five personality traits test. We found strong correlations between particular personality traits and different strategies of removing the findings of static code analysis as well as between personality and tool utilization. Based on that, we offer take-away improvement suggestions. Our results imply that developers should be aware of these solution strategies and use this information to build tools that are more appealing to people with different prevalent personality traits.
AB - There are many techniques to improve software quality. One is using automatic static analysis tools. We have observed, however, that despite the low-cost help they offer, these tools are underused and often discourage beginners. There is evidence that personality traits influence the perceived usability of a software. Thus, to support beginners better, we need to understand how the workflow of people with different prevalent personality traits using these tools varies. For this purpose, we observed users' solution strategies and correlated them with their prevalent personality traits in an exploratory study with student participants within a controlled experiment. We gathered data by screen capturing and chat protocols as well as a Big Five personality traits test. We found strong correlations between particular personality traits and different strategies of removing the findings of static code analysis as well as between personality and tool utilization. Based on that, we offer take-away improvement suggestions. Our results imply that developers should be aware of these solution strategies and use this information to build tools that are more appealing to people with different prevalent personality traits.
KW - Learning
KW - Personality
KW - Software engineering
KW - Static analysis
UR - http://www.scopus.com/inward/record.url?scp=84974555916&partnerID=8YFLogxK
U2 - 10.1145/2897586.2897599
DO - 10.1145/2897586.2897599
M3 - Conference contribution
AN - SCOPUS:84974555916
T3 - Proceedings - 9th International Workshop on Cooperative and Human Aspects of Software Engineering, CHASE 2016
SP - 75
EP - 81
BT - Proceedings - 9th International Workshop on Cooperative and Human Aspects of Software Engineering, CHASE 2016
PB - Association for Computing Machinery, Inc
T2 - 9th International Workshop on Cooperative and Human Aspects of Software Engineering, CHASE 2016
Y2 - 16 May 2016
ER -