|Authors||H. C. Benestad and J. E. Hannay|
|Title||Does the Prioritization Technique Affect Stakeholders' Selection of Essential Software Product Features?|
|Afilliation||Software Engineering, Software Engineering|
|Publication Type||Proceedings, refereed|
|Year of Publication||2012|
|Conference Name||Proceedings of IEEE International Conference on Empirical Software Engineering and Measurement (ESEM 2012)|
|Place Published||New York, NY, USA|
Context: To select the essential, non-negotiable product features is a key skill for stakeholders in software projects. Such selection relies on human judgment, possibly supported by structured prioritization techniques and tools. Goal: Our goal was to investigate whether certain attributes of prioritization techniques affect stakeholders' threshold for judging product features as essential. The four investigated techniques represent four combinations of granularity (low, high) and cognitive support (low, high). Method: To control for robustness and masking effects when investigating in the field, we conducted both an artificial experiment and a field experiment using the same prioritization techniques. In the artificial experiment, 94 subjects in four treatment groups indicated the features (from a list of 16) essential when buying a new cell phone. In the field experiment, 44 domain experts indicated the software product features that were essential for the fulfillment of the project's vision. The effects of granularity and cognitive support on the number of essential ratings were analyzed and compared between the experiments. Result: With lower granularity, significantly more features were rated as essential. The effect was large in the general experiment and extreme in the field experiment. Added cognitive support had medium effect, but worked in opposite directions in the two experiments, and was not statistically significant in the field experiment. Implications: Software projects should avoid taking stakeholders' judgments of essentiality at face value. Practices and tools should be designed to counteract biases and to support the conscious knowledge-based elements of prioritizing.