Managing the software process
Estimeetings: Development Estimates and a Front-End Process for a Large Project
IEEE Transactions on Software Engineering
Effort estimation using analogy
Proceedings of the 18th international conference on Software engineering
Cost estimation of software intensive projects: a survey of current practices
ICSE '91 Proceedings of the 13th international conference on Software engineering
An experimental study of individual subjective effort estimation and combinations of the estimates
Proceedings of the 20th international conference on Software engineering
Software Metrics: Measurement for Software Process Improvement
Software Metrics: Measurement for Software Process Improvement
Software Engineering Economics
Software Engineering Economics
Software Cost Estimation with Cocomo II with Cdrom
Software Cost Estimation with Cocomo II with Cdrom
Combination of software development effort prediction intervals: why, when and how?
SEKE '02 Proceedings of the 14th international conference on Software engineering and knowledge engineering
Web Development: Estimating Quick-to-Market Software
IEEE Software
IEEE Software
Preliminary guidelines for empirical research in software engineering
IEEE Transactions on Software Engineering
Learning How to Improve Effort Estimation in Small Software Development Companies
COMPSAC '00 24th International Computer Software and Applications Conference
An empirical study of maintenance and development estimation accuracy
Journal of Systems and Software
METRICS '99 Proceedings of the 6th International Symposium on Software Metrics
Project estimation using Screenflow Engineering
SEEP '96 Proceedings of the 1996 International Conference on Software Engineering: Education and Practice (SE:EP '96)
A Review of Surveys on Software Effort Estimation
ISESE '03 Proceedings of the 2003 International Symposium on Empirical Software Engineering
A review of studies on expert estimation of software development effort
Journal of Systems and Software
Evidence-Based Guidelines for Assessment of Software Development Cost Uncertainty
IEEE Transactions on Software Engineering
Using planning poker for combining expert estimates in software projects
Journal of Systems and Software
Data accumulation and software effort prediction
Proceedings of the 2010 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement
The challenge of assessing and controlling complexity in a large portfolio of software systems
Proceedings of the 11th International Conference on Product Focused Software
Human judgement and software metrics: vision for the future
Proceedings of the 2nd International Workshop on Emerging Trends in Software Metrics
Information and Software Technology
On using planning poker for estimating user stories
Journal of Systems and Software
Expert Systems with Applications: An International Journal
ACM Transactions on Software Engineering and Methodology (TOSEM)
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The effort required to complete software projects is often estimated, completely or partially, using the judgment of experts, whose assessment may be biased. In general, such bias as there is seems to be towards estimates that are overly optimistic. The degree of bias varies from expert to expert, and seems to depend on both conscious and unconscious processes. One possible approach to reduce this bias towards over-optimism is to combine the judgments of several experts. This paper describes an experiment in which experts with different backgrounds combined their estimates in group discussion. First, 20 software professionals were asked to provide individual estimates of the effort required for a software development project. Subsequently, they formed five estimation groups, each consisting of four experts. Each of these groups agreed on a project effort estimate via the pooling of knowledge in discussion. We found that the groups submitted less optimistic estimates than the individuals. Interestingly, the group discussion-based estimates were closer to the effort expended on the actual project than the average of the individual expert estimates were, i.e., the group discussions led to better estimates than a mechanical averaging of the individual estimates. The groups’ ability to identify a greater number of the activities required by the project is among the possible explanations for this reduction of bias.