- The amount of time that is required to be devoted to resolving these defects and testing the changes will cause a strain on the schedule.
- And it is not just the effort change, a sudden high number of unanticipated defects will cause the team to wonder about the quality of the work done,
- Most important, will cause a lot of uncertainty about whether all the defects have been found, or are there many more to be found ?
- When more defects are found, the overall cycle of analyzing the defect, figuring out the problem, doing an impact analysis of the change, making the change, getting somebody to review the change, and then testing the change will cause a lot of strain.
- Some of these changes can be big, and will require more effort to validate the fix, and in some of these cases, the team management will decide that they would not want to take a risk and would rather that the defect get passed onto customers.
The above was just an example of what happens when there are a large number of defects that suddenly crop up. However, it would be criminal on the part of the team management and the project manager if they have not already done a study of the kind of defects that come up in different stages of the development cycle. The best way to do this, and do some kind of forecasting, is to look at the bug curves of previous cycles (which obviously cannot be done if the data about defects of previous cycles is not taken at that time, or stored at a later point of time).
We had started doing this for the past 2-3 years or so, and this helped us determine what points of the development cycle had the highest number of defects that were found, as well as closed, and even which were the times when there was the highest chances of defects not being fixed (either being partially fixed or being rejected totally by the tester). Now, even though every cycle would be different, there were some patterns that had a high probability of being repeated (and this even true when one project differs from the other, although it varied from project to project).
Let us take another example. There was a time in the project when defects had a higher chance of being rejected, and a rejected defect can be very expensive in terms of time of both the developer and the tester. As a result, during such stages that had happened in previous projects, we would ensure that there was higher focus on impact analysis and code review, and had even borrowed some senior developers from another project for around a month just for the additional focus on reviews. This paid off, since the number of defects getting rejected went down considerably, which per single defect did not amount for much, but our analysis showed that the total saving of effort because of this additional effort was around 25%.
Read more about this effort in the next post (Look at bug curves for previous projects and identify patterns - Part 2)