A measurement framework for assessing the maturity of requirements engineering process
Software Quality Control
Towards software process patterns: An empirical analysis of the behavior of student teams
Information and Software Technology
A Model for Requirements Change Management: Implementation of CMMI Level 2 Specific Practice
PROFES '08 Proceedings of the 9th international conference on Product-Focused Software Process Improvement
Applying moving windows to software effort estimation
ESEM '09 Proceedings of the 2009 3rd International Symposium on Empirical Software Engineering and Measurement
Integrate the GM(1,1) and Verhulst models to predict software stage effort
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Data accumulation and software effort prediction
Proceedings of the 2010 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement
ACSC '09 Proceedings of the Thirty-Second Australasian Conference on Computer Science - Volume 91
PROFES'06 Proceedings of the 7th international conference on Product-Focused Software Process Improvement
EASE'09 Proceedings of the 13th international conference on Evaluation and Assessment in Software Engineering
EASE'08 Proceedings of the 12th international conference on Evaluation and Assessment in Software Engineering
Revisiting software development effort estimation based on early phase development activities
Proceedings of the 10th Working Conference on Mining Software Repositories
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Estimating the effort required for software process activities continues to present difficulties for software engineers, particularly given the uncertainty and subjectivity associated with the many factors that can influence effort. It is therefore advisable that managers review their estimates and plans on an ongoing basis during each project so that growing certainty can beharnessed in order to improve their management of future project tasks. In this paper we investigate the potential of using effort data recorded for completed project tasks to predict the effort needed for subsequent activities. Our approach is tested against data collected from sixteen projects undertaken by a single organization over a period of eighteen months. Our findings suggest that, at least in this case, the idea that there are standard proportions' of effort for particular development activities does not apply. Estimating effort on this basis would not have improved the management of these projects. We did find, however, that in most cases simple linear regression enabled us to produce better estimates than those provided by the project managers. Moreover, combining the managers' estimates with those produced by regression modeling also led to improvements in predictive accuracy. These results indicate that, in this organization, prior-phase effort data could be used to augment the estimation process already in place in order to improve the management of subsequent process tasks. This provides further confirmation of the value of local data and the benefits of quite simple quantitative analysis methods.