Traditional methods of generating quality code indicators (e.g. linear regression, decision tree induction) can be demonstrated to be inappropriate for IV&V purposes. IV&V is a unique aspect of the software lifecycle, and different methods are necessary to produce quick and accurate results. If quality code indicators could be produced on a per-project basis, then IV&V could proceed in a more straight-forward fashion, saving time and money. We present one case study on just such a project, showing that by using the proper metrics and machine learning algorithms, quality indicators can be found as early as 3 months into the IV&V process.
['Tim Menzies', 'J.S. Di Stefano', 'Marsha Chapman']
Learning early lifecycle IV & V quality indicators