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Displaying 1-7 out of 7 total
On the Value of Ensemble Effort Estimation
Found in: IEEE Transactions on Software Engineering
By Ekrem Kocaguneli,Tim Menzies,Jacky W. Keung
Issue Date:November 2012
pp. 1403-1416
Background: Despite decades of research, there is no consensus on which software effort estimation methods produce the most accurate models. Aim: Prior work has reported that, given M estimation methods, no single method consistently outperforms all others...
 
How to Find Relevant Data for Effort Estimation?
Found in: Empirical Software Engineering and Measurement, International Symposium on
By Ekrem Kocaguneli,Tim Menzies
Issue Date:September 2011
pp. 255-264
Background: Building effort estimators requires the training data. How can we find that data? It is tempting to cross the boundaries of development type, location, language, application and hardware to use existing datasets of other organizations. However,...
 
Experiences on Developer Participation and Effort Estimation
Found in: Software Engineering and Advanced Applications, Euromicro Conference
By Ekrem Kocaguneli,Ayse T. Misirli,Bora Caglayan,Ayse Bener
Issue Date:September 2011
pp. 419-422
Software effort estimation is critical for resource allocation and planning. Accurate estimates enable managers to distribute the workload among resources in a balanced manner. The actual workload of developers may be different from the values observed in ...
 
AI-Based Models for Software Effort Estimation
Found in: Software Engineering and Advanced Applications, Euromicro Conference
By Ekrem Kocaguneli, Ayse Tosun, Ayse Bener
Issue Date:September 2010
pp. 323-326
Decision making under uncertainty is a critical problem in the field of software engineering. Predicting the software quality or the cost/ effort requires high level expertise. AI based predictor models, on the other hand, are useful decision making tools ...
 
Building a second opinion: learning cross-company data
Found in: Proceedings of the 9th International Conference on Predictive Models in Software Engineering (PROMISE '13)
By Bojan Cukic, Ekrem Kocaguneli, Huihua Lu, Tim Menzies
Issue Date:October 2013
pp. 1-10
Background: Developing and maintaining a software effort estimation (SEE) data set within a company (within data) is costly. Often times parts of data may be missing or too difficult to collect, e.g. effort values. However, information about the past proje...
     
Size doesn't matter?: on the value of software size features for effort estimation
Found in: Proceedings of the 8th International Conference on Predictive Models in Software Engineering (PROMISE '12)
By Byeong Ho Kang, Ekrem Kocaguneli, Jairus Hihn, Tim Menzies
Issue Date:September 2012
pp. 89-98
Background: Size features such as lines of code and function points are deemed essential for effort estimation. No one questions under what conditions size features are actually a "must". Aim: To question the need for size features and to propose a method ...
     
When to use data from other projects for effort estimation
Found in: Proceedings of the IEEE/ACM international conference on Automated software engineering (ASE '10)
By Ekrem Kocaguneli, Gregory Gay, Jacky W. Keung, Tim Menzies, Ye Yang
Issue Date:September 2010
pp. 321-324
Collecting the data required for quality prediction within a development team is time-consuming and expensive. An alternative to make predictions using data that crosses from other projects or even other companies. We show that with/without relevancy filte...
     
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