None with this specific data set. But for older work on similar data, see: “Validation Methods for Calibrating Software Effort Models”, T. Menzies and D. Port and Z. Chen and J. Hihn and S. Stukes, Proceedings ICSE 2005,http://menzies.us/pdf/04coconut.pdf Results: Given background knowledge on 60 prior projects, a new cost model can be tuned to local data using as little as 20 new projects. A very simple calibration method (COCONUT) can achieve PRED(30)=7% or PRED(20)=50% (after 20 projects). These are results seen in 30 repeats of an incremental cross-validation study. Two cost models are compared; one based on just lines of code and one using over a dozen “effort multipliers”. Just using lines of code loses 10 to 20 PRED(N) points. Additional Usage: “Feature Subset Selection Can Improve Software Cost Estimation Accuracy” Zhihao Chen, Tim Menzies, Dan Port and Barry Boehm Proceedings PROMISE Workshop 2005,http://promise.site.uottawa.ca/proceedings/pdf/1.pdf P02, P03, P04 are used in this paper. Results To the best of our knowledge, this is the first report of applying feature subset selection (FSS) to software effort data. FSS can dramatically improve cost estimation. T-tests are applied to the results to demonstrate that always in our data sets, removing attributes improves performance without increasing the variance in model behavior.

Instances: 93 Attributes: 24 -15 standard COCOMO-I discrete attributes in the range Very_Low to Extra_High -7 others describing the project -one lines of code measure -one goal field being the actual effort in person months.