@misc{15160,
  author = {Martin Shepperd and Carolyn Mair and Magne J{\o}rgensen},
  title = {An Experimental Evaluation of a De-biasing Intervention for Professional Software Developers},
  abstract = {CONTEXT: The role of expert judgement is essential in our quest to improve software project planning and execution. However, its accuracy is dependent on many factors, not least the avoidance of judgement biases, such as the anchoring bias, arising from being influenced by initial information, even when it{\textquoteright}s misleading or irrelevant. This strong effect is widely documented. OBJECTIVE: We aimed to replicate this anchoring bias using professionals and, novel in a software engineering context, explore de-biasing interventions through increasing knowledge and awareness of judgement biases. METHOD: We ran two series of experiments in company settings with a total of 410 software developers. Some developers took part in a workshop to heighten their awareness of a range of cognitive biases, including anchoring. Later, the anchoring bias was induced by presenting low or high productivity values, followed by the participants{\textquoteright} estimates of their own project productivity. Our hypothesis was that the workshop would lead to reduced bias, i.e., work as a de-biasing intervention. RESULTS: The anchors had a large effect (robust Cohen{\textquoteright}s d=1.19) in influencing estimates. This was substantially reduced in those participants who attended the workshop (robust Cohen{\textquoteright}s d=0.72). The reduced bias related mainly to the high anchor. The de-biasing intervention also led to a threefold reduction in estimate variance. CONCLUSIONS: The impact of anchors upon judgement was substantial. Learning about judgement biases does appear capable of mitigating, although not removing, the anchoring bias. The positive effect of de-biasing through learning about biases suggests that it has value.},
  year = {2018},
  journal = {SAC 2018: Symposium on Applied Computing},
  publisher = {ACM},
  address = {New York},
}