# Software Development Process Considerations in GNSS-Denied Navigation Project for Drones
## 4 Process Metrics
### Proposed Metrics to Evaluate Process Suitability in a Given Environment  
For the software development process explained in this paper, we found a solution to assess whether the process was suitable for our environment. The process was executed in an environment characterised by its features mentioned at the beginning of this chapter. The project environment had a big impact on overall work organisation and success. Therefore, it was important to make sure the new approach was not a huge wave that would lead the team to a first struggle but that would help the team \"surf on the waves\". Our research on that topic led us to work out a metric that allowed us to address process suitability in our specific environment and to propose a three-degree scale to help us assess whether the process was friendly. Figure 9: Required vs. actual team velocity. Impact on schedule. For the first, we needed to find a formula that helped us compute the degree of sticking to the process, because we noticed that not everyone was following the process exactly as planned. The formula is:  
\[Stp=\left(1-\frac{\sum_{i}^{i}e}{i*n}\right)*100\%\]  
where  
\(Stp\) = degree of sticking to the process. Expressed in [%], rounded to integer type. \(i\) = number of closed iterations. \(e\) = number of individuals not sticking to the process in an iteration (estimated after iteration). If in a given iteration there are two individuals not following the process, the value is increased by two, if there are five individuals, the value is increased by five, and so on. For zero individuals, the value is not increased. \(n\) = number of individuals in the development team (team size). The numbers of individuals not sticking to the process were collected in each retrospective activity when it was found that some activities did not follow the process for any reason. It was summed up after a few iterations. This was not the number of mistakes made, but this value corresponded to the fact that an individual made any mistake or variation in a given iteration. Even if we observed that one individual made six variations or mistakes in a given iteration, the value increased by one. There was no reason to calculate the degree of sticking to the process (\(Stp\)) in the few first iterations, as there should be some time to stabilise and become used to the new work style. We noticed that, in our case, we could achieve more reliable values of the degree of sticking to the process after three iterations (6 weeks). The information about the time required to stabilise and become used to the new work style may also be used to assess the learnability of the process and whether it needed any changes or improvements. This was, however, beyond the scope of our interest regarding the given process. To support an assessment of process suitability in a given environment, we proposed a dedicated three-degree matrix regarding the computed \(Stp\) value, as shown in Table 1.  
As a result of comparing the \(Stp\) value to given ranges, we obtained three possible **degrees of process suitability** (and a short name \"suitability degree\"): first, second and third degree. The **degree of process suitability**, determined by the matrix presented in Table 1, helped us to improve our process and to take any supporting actions when needed. Supporting actions that impacted the \(Stp\) value are described in Section 5. In our case, the value of \(Stp\) after three iterations (6 weeks), reached the third degree of suitability, but was close to 75%, which was finally increased by incorporating supporting actions described further.