I have looked and read in the internet that using static code analysis data, we can predict what modules in the application are defect prone. When I try to imagine of dataset,it is getting difficult to fit it as 2 dimensional. Since each build can have many modules and for each module we will be having source code metrics and defects count. My doubts are
- Can we some how build a meaningful 2D dataset for analysis (like regression) and prediction ?
- Is it just a theory or machine learning is being actually leveraged for this ? If yes, what techniques/use cases are currently under use ?
- What are the other practical factors that needs to be considered for defect prediction analysis and can we collect them in reality ?
It will be very helpful if you could point out some detailed articles on this,if any.
PS: I am posting this here because people with great expertise in testing can only advise on where to look at for defects.