An empirical validation of software cost estimation models
Communications of the ACM
Cost estimation of software intensive projects: a survey of current practices
ICSE '91 Proceedings of the 13th international conference on Software engineering
Estimating Software Project Effort Using Analogies
IEEE Transactions on Software Engineering
Experimentation in software engineering: an introduction
Experimentation in software engineering: an introduction
Software Engineering Economics
Software Engineering Economics
A review of studies on expert estimation of software development effort
Journal of Systems and Software
Journal of Systems and Software
Inconsistency of expert judgment-based estimates of software development effort
Journal of Systems and Software
Tests for consistent measurement of external subjective software quality attributes
Empirical Software Engineering
Selection of strategies in judgment-based effort estimation
Journal of Systems and Software
Information Systems Research
Information and Software Technology
Investigating intentional distortions in software cost estimation - An exploratory study
Journal of Systems and Software
EASE'08 Proceedings of the 12th international conference on Evaluation and Assessment in Software Engineering
PROFES'12 Proceedings of the 13th international conference on Product-Focused Software Process Improvement
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Anchoring and adjustment is a form of cognitive bias that affects judgments under uncertainty. If given an initial answer, the respondent seems to use this as an 'anchor', adjusting it to reach a more plausible answer, even if the anchor is obviously incorrect. The adjustment is frequently insufficient and so the final answer is biased. In this paper, we report a study to investigate the effects of this phenomenon on software estimation processes. The results show that anchoring and adjustment does occur in software estimation, and can significantly change the resulting estimates, no matter what estimation technique is used. The results also suggest that, considering the magnitude of this bias, software estimators tend to be too confident of their own estimations.