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Improving Analogy Software Effort Estimation using Fuzzy Feature Subset Selection Algorithm

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Improving Analogy Software Effort Estimation using Fuzzy Feature Subset Selection Algorithm - PROMISE 2008

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IMPROVING ANALOGY SOFTWARE EFFORT ESTIMATION USING FUZZY FEATURE SUBSET SELECTION ALGORITHM Mohammad Azzeh [email_address] Dr. Daniel Neagu [email_address] Prof. Peter Cowling [email_address] Computing Department, School of informatics University of Bradford Promise’08
Agenda ,[object Object],[object Object],[object Object],[object Object],[object Object]
Motivation ,[object Object],[object Object],[object Object],[object Object]
The Problem ,[object Object],[object Object],[object Object],[object Object],[object Object]
Feature Subset Selection (FSS) ,[object Object],[object Object],[object Object],[object Object],Dataset D(K x M) FSS Dataset D(K x N) Where N<M
FSS in Software estimation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The proposed Solution ,[object Object],[object Object],[object Object],[object Object],[object Object]
The proposed Solution Using Fuzzy c-Means Using Partition matrix and clusters centres Data set with M features Select a subset feature with N dimension
The Proposed Solution ,[object Object],[object Object],[object Object]
FFSS algorithm ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Empirical validation ,[object Object],[object Object],[object Object],Dataset Number of feature Number of Projects ISBSG (release 10) 14 400 Desharnais 10 77
Kirsopp & Shepperd said: ,[object Object]
Results...ISBSG Algorithm used in analogy model One analogy mean of 2 analogies mean of 3 analogies All features 37.74% 41.0% 29.4% Exhaustive search, Hill climbing, Random search 28.25% 30.3% 30.2% Forward subset selection 33.3% 30.4% 31.2% Backward subset selection 34.7% 38% 34.4% FFSS 28.7% 30.6% 32.2%
Results...ISBSG Algorithm used in analogy model One analogy mean of 2 analogies mean of 3 analogies All features 31.6% 33% 20.62% Exhaustive search. Hill climbing, Random search 21.9% 24% 20.8% Forward subset selection 21.0% 21.4% 20.7% Backward subset selection 22.6% 28.7% 25.2% FFSS 21.8% 22.3% 22.7%
Results...Desharnais Algorithm used in analogy model One analogy mean of 2 analogies mean of 3 analogies All features 60.1% 51.5% 50.0% Exhaustive search, Forward subset selection, Hill climbing, Random search 38.2% 39.4% 36.4% Backward subset selection 42.4% 43.9% 46.6% FFSS 40.2% 40.3% 38.5%
Results...Desharnais Algorithm used in analogy model One analogy mean of 2 analogies mean of 3 analogies All features 41.7% 41.0% 36.1% Exhaustive search, Forward subset selection Hill climbing, Random search 30.8% 38.0% 30.9% Backward subset selection 38.4% 37.4% 34.6% FFSS 32.4% 33.3% 31.7%
Conclusions ,[object Object],[object Object],[object Object]
Conclusions…cont Which FSS is suitable ? -Fuzzy Feature subset selection -Forward feature selection, -Backward features selection Exhaustive search and Hill Climbing Accuracy only Less time, and quit reasonable Accuracy And data set is large Dataset size Random Hill Climbing Small Large
Threats to experiment validity ,[object Object],[object Object],[object Object],[object Object]
References ,[object Object],[object Object]
[object Object]

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Improving Analogy Software Effort Estimation using Fuzzy Feature Subset Selection Algorithm

  • 1. IMPROVING ANALOGY SOFTWARE EFFORT ESTIMATION USING FUZZY FEATURE SUBSET SELECTION ALGORITHM Mohammad Azzeh [email_address] Dr. Daniel Neagu [email_address] Prof. Peter Cowling [email_address] Computing Department, School of informatics University of Bradford Promise’08
  • 2.
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  • 4.
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  • 6.
  • 7.
  • 8. The proposed Solution Using Fuzzy c-Means Using Partition matrix and clusters centres Data set with M features Select a subset feature with N dimension
  • 9.
  • 10.
  • 11.
  • 12.
  • 13. Results...ISBSG Algorithm used in analogy model One analogy mean of 2 analogies mean of 3 analogies All features 37.74% 41.0% 29.4% Exhaustive search, Hill climbing, Random search 28.25% 30.3% 30.2% Forward subset selection 33.3% 30.4% 31.2% Backward subset selection 34.7% 38% 34.4% FFSS 28.7% 30.6% 32.2%
  • 14. Results...ISBSG Algorithm used in analogy model One analogy mean of 2 analogies mean of 3 analogies All features 31.6% 33% 20.62% Exhaustive search. Hill climbing, Random search 21.9% 24% 20.8% Forward subset selection 21.0% 21.4% 20.7% Backward subset selection 22.6% 28.7% 25.2% FFSS 21.8% 22.3% 22.7%
  • 15. Results...Desharnais Algorithm used in analogy model One analogy mean of 2 analogies mean of 3 analogies All features 60.1% 51.5% 50.0% Exhaustive search, Forward subset selection, Hill climbing, Random search 38.2% 39.4% 36.4% Backward subset selection 42.4% 43.9% 46.6% FFSS 40.2% 40.3% 38.5%
  • 16. Results...Desharnais Algorithm used in analogy model One analogy mean of 2 analogies mean of 3 analogies All features 41.7% 41.0% 36.1% Exhaustive search, Forward subset selection Hill climbing, Random search 30.8% 38.0% 30.9% Backward subset selection 38.4% 37.4% 34.6% FFSS 32.4% 33.3% 31.7%
  • 17.
  • 18. Conclusions…cont Which FSS is suitable ? -Fuzzy Feature subset selection -Forward feature selection, -Backward features selection Exhaustive search and Hill Climbing Accuracy only Less time, and quit reasonable Accuracy And data set is large Dataset size Random Hill Climbing Small Large
  • 19.
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  • 21.