No abstract

After data mining National Aeronautics and Space Administration (NASA) independent verification and validation (IV&V) data, we offer (a) an early life cycle predictor for project issue frequency and severity; (b) an IV&V task selector (that used the predictor to find the appropriate IV&V tasks); and (c) pruning heuristics describing what tasks to ignore, if the budget cannot accommodate all selected tasks. In ten-way cross-validation experiments, the predictor performs very well indeed: the average f -measure for predicting four classes of issue severity was over 0.9. This predictor is built using public-domain data and software. To the best of our knowledge, this is the first reproducible report of a predictor for issue frequency and severity that can be applied early in the life cycle.