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Nearest Neighbor Sampling for Better Defect Prediction Gary D. Boetticher Department of Software Engineering University of Houston - Clear Lake Houston, Texas, USA

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The Problem: Why is there not more ML in Software Engineering? Human-Based 62 to 86% [Jørgensen 2004] Algorithmic Machine Learning 7 to 16%

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Key Idea More ML in SE through a more defined experimental process.

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Agenda A better defined process for better predicting (quality) Experiments: Nearest Neighbor Sampling on PROMISE Defect data sets Extending the approach Discussion Conclusions

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A Better Defined Process Emphasis of ML approaches Emphasis on Measuring Success – PRED(X) – Accuracy – MARE Prediction success depends upon the relationship between training and test data.

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PROMISE Defect Data (from NASA) 21 Inputs – Size (SLOC, Comments) – Complexity (McCabe Cyclomatic Comp.) – Vocabulary (Halstead Operators, Operands) 1 Output: Number of Defects

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Data Preprocessing Reduced to 2 classes

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Experiment 1 6904 with 0 Defects 2007 with 1+ Defects JM1 } 22% Training 40% of Original Data Nice TestNasty Test

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Experiment 1 Continued Training Nice Test Nasty Test Remaining Vectors from Data set Remaining Vectors from Data set

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J48 and Naïve Bayes Classifiers from WEKA 200 Trials (100 Nice Test Data + 100 Nasty Test Data) – CM1 – JM1 – KC1 – KC2 – PC1 Experiment 1 Continued 20 Nice Trials + 20 Nasty Trials

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Results: Accuracy

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Results: Average Confusion Matrix Average Nice Results Average Nasty Results Note the distribution: 0 Defects 1+ Defects

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Experiment 2: 60% Train, KNN=3

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Assessing Experiment Difficulty Exp_Difficulty = 1 - Matches / Total_Test_Instances Match = Test vector’s nearest neighbor is from the same class instance in the training set. Experimental Difficulty = 1 Experimental Difficulty = 0 Hard experiment Easy experiment

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Assessing Overall Data Difficulty Overall Data Difficulty = 1 - Matches / Total_Data_Instances Match = A data vector’s nearest neighbor is from the same class instance as another vector in the data set. Overall Data Difficulty = 1 Overall Data Difficulty = 0 Difficult Data Easy Data

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Discussion: Anticipated Benefits Method for characterizing difficulty of experiment More realistic models Easy to implement Can be integrated into N-Way Cross Validation Can apply to various types of SE data sets: – Defect Prediction – Effort Estimation Can be extended beyond SE to other domains

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Discussion: Potential Problems More work needs to be done Agreement on how to measure Experimental Difficulty Extra overhead Implicitly or Explicitly Data Staved Domain

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How to get more ML in SE? Conclusions Assess experiments/data for their difficulty Benefits: More credibility to the modeling process More reliable predictors More realistic models

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Thanks to the reviewers for their comments! Acknowledgements

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1) M. Jørgensen, A Review of Studies on Expert Estimation of Software Development Effort, Journal Systems and Software, Vol 70, Issues 1-2, 2004, Pp. 37-60. References

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