Published February 8, 2008
| Version v1
Dataset
Open
nasa93
Creators
Description
- 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.
Notes
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.
Files
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