In the long-run, queueing systems tend to follow Little's law given by
L = λW
where L is the long-term average number of customers waiting (in a stationary system), λ is the long-term average arrival rate of customers and W is the average time to serve a customer.
In software development, you can reduce W through best practices, standards, better specs, improved testing, increasing team size etc., You can control λ by triaging tickets.
This works as long as the customers (in the s/w case tickets or PRs) are all independent.
A lot of the complexity in s/w development results from interdependencies between tickets. This causes the service times to vary and pushes the long-term W higher and not to mention affecting the stationary assumption of the system.
In software development, you can reduce W through best practices, standards, better specs, improved testing, increasing team size etc., You can control λ by triaging tickets.
This works as long as the customers (in the s/w case tickets or PRs) are all independent.
A lot of the complexity in s/w development results from interdependencies between tickets. This causes the service times to vary and pushes the long-term W higher and not to mention affecting the stationary assumption of the system.