Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Authors' reply to 'comments on 'researcher bias: The use of machine learning in software defect prediction''
AU - Shepperd, M.
AU - Hall, T.
AU - Bowes, D.
PY - 2018/11/1
Y1 - 2018/11/1
N2 - In 2014 we published a meta-analysis of software defect prediction studies [1] . This suggested that the most important factor in determining results was Research Group, i.e., who conducts the experiment is more important than the classifier algorithms being investigated. A recent re-analysis [2] sought to argue that the effect is less strong than originally claimed since there is a relationship between Research Group and Dataset. In this response we show (i) the re-analysis is based on a small (21 percent) subset of our original data, (ii) using the same re-analysis approach with a larger subset shows that Research Group is more important than type of Classifier and (iii) however the data are analysed there is compelling evidence that who conducts the research has an effect on the results. This means that the problem of researcher bias remains. Addressing it should be seen as a matter of priority amongst those of us who conduct and publish experiments comparing the performance of competing software defect prediction systems.
AB - In 2014 we published a meta-analysis of software defect prediction studies [1] . This suggested that the most important factor in determining results was Research Group, i.e., who conducts the experiment is more important than the classifier algorithms being investigated. A recent re-analysis [2] sought to argue that the effect is less strong than originally claimed since there is a relationship between Research Group and Dataset. In this response we show (i) the re-analysis is based on a small (21 percent) subset of our original data, (ii) using the same re-analysis approach with a larger subset shows that Research Group is more important than type of Classifier and (iii) however the data are analysed there is compelling evidence that who conducts the research has an effect on the results. This means that the problem of researcher bias remains. Addressing it should be seen as a matter of priority amongst those of us who conduct and publish experiments comparing the performance of competing software defect prediction systems.
U2 - 10.1109/TSE.2017.2731308
DO - 10.1109/TSE.2017.2731308
M3 - Journal article
VL - 44
SP - 1129
EP - 1131
JO - IEEE Transactions on Software Engineering
JF - IEEE Transactions on Software Engineering
SN - 0098-5589
IS - 11
ER -