I have studied Statistics as a student and then spent most of my professional life doing software testing! It's always been my curiosity to find out how analytics can help in identifying various patterns while testing a software under development. One of the specific areas that always attracted me was the test management where I thought inferences about the performances of the development and testing teams could be drawn based on statistical data analysis. And, as part of that investigation, I found out that the information contained in the logged defects tell us many stories.
While looking at the defect metrics, I always made the following assumptions about existing defects:
So, while doing testing, I constructed normal distribution for each of the 4 sets of data (i.e., for New, Open/Reopen, Fixed & Retest) on a daily basis to understand how this duration is increasing or decreasing over time. And to to do this, I used box plot diagrams. Following is a example where box plots are constructed on a daily basis:
Note that, the individual boxes are separate normal distributions and viewed from the top. Anyway, I kept plotting the distributions in this way for all of the 4 sets of data (i.e., for New, Open/Reopen, Fixed & Retest) separately. This helped me identifying the pattern and to guess the root cause of the problem so that I could take necessary actions to bring the speed and quality of testing on track, as and when needed.
Abhimanyu Gupta is the co-founder & President of Testing Algorithms. His areas of interest are innovating new algorithms and processes to make software testing more effective & efficient.