The document discusses building human-based software estimation models that are accurate, intuitive, and easy to understand. It presents an approach using correlation and scale factors between estimated and actual effort. Experiments on a dataset of 178 samples show that combining correlation and scale factors into a decision tree achieves up to 93.3% accuracy. The resulting model bridges expert and algorithmic estimation methods.
1. Using Correlation and Accuracy for Identifying Good Estimators http:// nas.cl.uh.edu/boetticher/publications.html The 4 th International Predictor Models in Software Engineering (PROMISE) Workshop Gary D. Boetticher Nazim Lokhandwala Univ. of Houston - Clear Lake, Houston, TX, USA [email_address] [email_address] 63 62 61
7. Feedback to Users http://nas.cl.uh.edu/boetticher/publications.html The 4 th International Predictor Models in Software Engineering (PROMISE) Workshop How user compares to other respondents User’s Estimates Actual Estimates
8. Experiments: Data http://nas.cl.uh.edu/boetticher/publications.html The 4 th International Predictor Models in Software Engineering (PROMISE) Workshop Original Data set Experiment 1 Experiment 2 Experiment 3 82.8 -29.4 0.008 29X Correlation S c a l e Correlation S c a l e Correlation S c a l e Correlation S c a l e
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10. Results: Correlation Only http://nas.cl.uh.edu/boetticher/publications.html The 4 th International Predictor Models in Software Engineering (PROMISE) Workshop 2-Class Problem: 10 Best (A), 10 Worst (F) 1000 Trials, Accuracy = 41.6% Attribute Reduction using WRAPPER 1000 Trials, Accuracy = 78.6%
11. Results: Scale Factor Only http://nas.cl.uh.edu/boetticher/publications.html The 4 th International Predictor Models in Software Engineering (PROMISE) Workshop 1000 Trials, Accuracy = 65.0% Attribute Reduction using WRAPPER 1000 Trials, Accuracy = 78.2% 2-Class Problem: 10 Best (A), 10 Worst (F)
12. Results: Correlation & Scale Factor http://nas.cl.uh.edu/boetticher/publications.html The 4 th International Predictor Models in Software Engineering (PROMISE) Workshop 1000 Trials, Accuracy = 82.2% Attribute Reduction using WRAPPER 1000 Trials, Accuracy = 93.3% 2-Class Problem: 10 Best (A), 10 Worst (F)
13. Discussion - 1 http://nas.cl.uh.edu/boetticher/publications.html The 4 th International Predictor Models in Software Engineering (PROMISE) Workshop How well does the decision tree from the third experiment apply to all the respondents minus outliers? Best Estimators Poorest Estimators Average Correlation 0.4173 0.3686 Average Scale Factor 2.6198 2.7419
![Using Correlation and Accuracy for Identifying Good Estimators http:// nas.cl.uh.edu/boetticher/publications.html The 4 th International Predictor Models in Software Engineering (PROMISE) Workshop Gary D. Boetticher Nazim Lokhandwala Univ. of Houston - Clear Lake, Houston, TX, USA [email_address] [email_address] 63 62 61](https://image.slidesharecdn.com/boetticherpresentationpromise2008v2-1210606628798948-8/85/Boetticher-Presentation-Promise-2008v2-1-320.jpg)
![Research vs. Reality according to Jörgensen ,[object Object],[object Object],http://nas.cl.uh.edu/boetticher/publications.html The 3 rd International Predictor Models in Software Engineering (PROMISE) Workshop JSS ’04: Compendium of expert estimation studies -89 89-99 00-04 Total Algorithm 48 137 70 255 ML 1 32 41 74 Human 3 22 21 46 Misc. 7 19 26 52 68% Algorithm 20% ML 12% Human Paper Human Hihn 91 89% Heemstra 91 62% Paynter 96 86% Jørgensen 97 84% Hill 00 100% Kitchenham 02 72% 82% Human 18% Formal](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)