I changed my runtime environment from 5 days to 7 days. I would have expected my MTTD value to get smaller because I am running the system longer. Why did my MTTD value get larger?

The MTTD value is simply the reciprocal of the monthly defect rate. Suppose the expected defect rate is 100 defects found during the last month of phase 3. This is a theoretical monthly rate number based on an average runtime environment. The MTTD for this month is therefore 1 month/100 errors or .01 months between defects. 

I sometimes get poor defect curve fits when running a forecast.  What can I do to improve this?

Getting a good curve fit is important - the poorer the fit, the less confidence you will have in the projected new time for that set of data. The goodness of fit is particularly important during the latter stages of Phase 3 because defect data is weighted more heavily at that time. There are two things you can do to improve your defect curve fits:

Why don't my staffing rate numbers add up to the same total shown on cumulative effort charts and reports?

First, it is important to understand the difference between the average staffing rate and cumulative effort charts and reports.

The Solution Panel says my peak staff value is 10.3, but the Average Staffing Rate charts and reports show a peak staff of 10.1. What is causing this inconsistency?

The theoretical Rayleigh curve used to derive the values on the Solution Panel is a smooth curve resulting in different staffing numbers at each point along the x-axis. In drawing the Average Staffing chart, this smooth theoretical curve is approximated by a series of discrete staffing rates (with one bar, or average staffing rate value, per month).

When I change the Person Hours per Person Month field in Accounting Options, there is no change to the total schedule and effort for the project.  How can I adjust the estimate to reflect overtime?

My actual MTTD data points start at different times depending on whether there is a forecast being displayed. Why?

MTTD is not truly relevant until the entire system is integrated and functioning as a complete entity.  For this reason, MTTD values are not shown until Systems Integration Test (which occurs at about 71% of phase 3).   

Sometimes the effort data points on my Life Cycle Sensitivity to PI graph have values that do not always increase (or decrease); e.g., there are small dips in the data.

The 50% solution is based on the expected value (the solution with a 50% probability of achieving the same or better values for schedule, effort, cost, and reliability). Each of the inputs to an estimate - size, application complexity, and productivity - has some uncertainty associated with it. If any one of your input parameters (size, for instance) increases significantly from the original estimate, it makes sense that the actual outcome will vary from the estimated outcome.

How does SLIM-Control handle missing actual data points?

The answer to this question depends on the context. You can display interpolated or missing data points on charts via the "display interpolated data" option in the chart properties. If possible, SLIM-Control will interpolate missing data points using the expected shape of the associated metric curve. Note that only interior data points can be interpolated - SLIM-Control needs a start and end point for interpolation.

What is the impact of selecting various phase staffing shapes? When should I deviate from the default Rayleigh pattern, and how is my estimate affected?

We have found that the default Rayleigh pattern is the staffing pattern that best matches the application of effort to the work to be performed but due to staffing constraints or different software management styles, you may decide that another staffing pattern fits your organization or project better.

In general, the various staffing shapes can be described as follows: