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Promise 2011: "An Iterative Semi-supervised Approach to Software Fault Prediction"

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CS, NcState
CS, NcState

Promise 2011: "An Iterative Semi-supervised Approach to Software Fault Prediction" Huihua Lu, Bojan Cukic and Mark Culp.

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An Iterative Semi-supervised Approach to Software Fault Prediction Huihua Lu, Bojan Cukic, Mark Culp Lane Department of Computer Science and Electrical Engineering Department of Statistics West Virginia University Morgantown, WV September 2011
Presentation Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Introduction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Goal of the Study ,[object Object],[object Object]
Presentation Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Semi-Supervised Learning-1 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Semi-Supervised Learning-2 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Related Work ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Presentation Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Methodology-1 ,[object Object],[object Object],[object Object],[object Object],Initialize the labels for U Reset the labels for L Fit the labels for U+L
Methodology-2 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Software Data Sets ,[object Object]
Performance Measures ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Presentation Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Experiments ,[object Object],[object Object],[object Object],[object Object],[object Object]
Presentation Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Results: PC 3
Results at threshold 0.5
At threshold 0.1
Overall Comparison
Presentation Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Summary ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Future Work ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Questions ,[object Object],[object Object],[object Object]

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Promise 2011: "An Iterative Semi-supervised Approach to Software Fault Prediction"

Notas do Editor

  1. WV
  2. WV
  3. The assumption of Supervised Learning is that the distribution of training data should be identical to the distribution of testing data. That is the training data should be presentive to the data space. WV
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  13. 1. Horizontal lines represent the performance of random forest. Supervised learning does not change with iteration… Mention that AUC does not change 2. Then observe that improvements in PD limited at low threshold because few FP modules remain to be detected Performance (PD) may slightly deteriorate at low thresholds (few %) WV
  14. Semi-supervised approach with a small number of labeled modules (2% or 5%) may not lead to improvement WV
  15. WV
  16. Lager size of Labeled data, better performance Overall, semi-supervised algorithm performs better than the corresponding supervised algorithm (RF) WV
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  19. WV