TY - GEN
T1 - An analysis of developer metrics for fault prediction
AU - Matsumoto, Shinsuke
AU - Kamei, Yasutaka
AU - Monden, Akito
AU - Matsumoto, Ken Ichi
AU - Nakamura, Masahide
PY - 2010/12/10
Y1 - 2010/12/10
N2 - Background: Software product metrics have been widely used as independent variables for constructing a fault prediction model. However, fault injection depends not only on characteristics of the products themselves, but also on characteristics of developers involved in the project. Aims: The goal of this paper is to study the effects of developer features on software reliability. Method: This paper proposes developer metrics such as the number of code churns made by each developer, the number of commitments made by each developer and the number of developers for each module. By using the eclipse project dataset, we experimentally analyzed the relationship between the number of faults and developer metrics. Second, the effective of developer metrics for performance improvements of fault prediction models were evaluated. Results: The result revealed that the modules touched by more developer contained more faults. Compared with conventional fault prediction models, developer metrics improved the prediction performance. Conclusions: We conclude that developer metrics are good predictor of faults and we must consider the human factors for improving the software reliability.
AB - Background: Software product metrics have been widely used as independent variables for constructing a fault prediction model. However, fault injection depends not only on characteristics of the products themselves, but also on characteristics of developers involved in the project. Aims: The goal of this paper is to study the effects of developer features on software reliability. Method: This paper proposes developer metrics such as the number of code churns made by each developer, the number of commitments made by each developer and the number of developers for each module. By using the eclipse project dataset, we experimentally analyzed the relationship between the number of faults and developer metrics. Second, the effective of developer metrics for performance improvements of fault prediction models were evaluated. Results: The result revealed that the modules touched by more developer contained more faults. Compared with conventional fault prediction models, developer metrics improved the prediction performance. Conclusions: We conclude that developer metrics are good predictor of faults and we must consider the human factors for improving the software reliability.
KW - Developer metrics
KW - Fault prediction
KW - Human factor
UR - http://www.scopus.com/inward/record.url?scp=78649777129&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78649777129&partnerID=8YFLogxK
U2 - 10.1145/1868328.1868356
DO - 10.1145/1868328.1868356
M3 - Conference contribution
AN - SCOPUS:78649777129
SN - 9781450304047
T3 - ACM International Conference Proceeding Series
BT - 6th International Conference on Predictive Models in Software Engineering, PROMISE 2010
T2 - 6th International Conference on Predictive Models in Software Engineering, PROMISE 2010
Y2 - 12 September 2010 through 13 September 2010
ER -