Mental models: towards a cognitive science of language, inference, and consciousness
Mental models: towards a cognitive science of language, inference, and consciousness
The sciences of the artificial (3rd ed.)
The sciences of the artificial (3rd ed.)
Software Requirements Prioritizing
ICRE '96 Proceedings of the 2nd International Conference on Requirements Engineering (ICRE '96)
Evaluating the practical use of different measurement scales in requirements prioritisation
Proceedings of the 2006 ACM/IEEE international symposium on Empirical software engineering
Empirical Software Engineering
Systematic review: A systematic review of effect size in software engineering experiments
Information and Software Technology
The Role of Deliberate Artificial Design Elements in Software Engineering Experiments
IEEE Transactions on Software Engineering
Tool-supported requirements prioritization: Comparing the AHP and CBRank methods
Information and Software Technology
Study of Prioritization Techniques Using Students as Subjects
ICIME '09 Proceedings of the 2009 International Conference on Information Management and Engineering
The effects of request formats on judgment-based effort estimation
Journal of Systems and Software
Perceived productivity threats in large agile development projects
Proceedings of the 2010 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement
A comparison of model-based and judgment-based release planning in incremental software projects
Proceedings of the 33rd International Conference on Software Engineering
IEEE Transactions on Software Engineering
A systematic literature review of software requirements prioritization research
Information and Software Technology
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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.