Slide 23
Slide 23 text
23 BUILD SOFTWARE TO TEST SOFTWARE
AI Testing Talks
Example: Software Defect Prediction
Number of instances: 10 885 modules. Creators: NASA, http://mdp.ivv.nasa.gov.
Hypotheses:
● code with complicated pathways are more error-prone
● code that is hard to read is more likely to be fault prone
● static measures are useful to guide software quality predictions
● static measures can never be a certain indicator of the presence of a fault
Number of attributes (dimensionality): 22
● 5 different lines of code measure
● 3 McCabe metrics (cyclomatic, essential, design complexity)
● 4 base Halstead measures (volume, length, difficulty, intelligence)
● 8 derived Halstead measures, a branch-count
● 1 goal field (module has/has not one or more reported defects)
https://www.kaggle.com/datasets/semustafacevik/software-defect-prediction