Why do many Software Reliability Growth Models use a non-homogeneous Poisson process for fault arrivals?

• A. Because it allows time-varying failure rate reflecting learning.
• B. Because it enforces a constant failure rate.
• C. Because it assumes no new faults.
• D. Because failures never decrease.

Answer: (A)

The main idea is that fault arrivals in software reliability growth models change over time. As testing progresses and defects are found and fixed, the pool of latent faults shrinks and the tester’s understanding of the system improves, so the rate at which new faults are detected tends to fall. A non-homogeneous Poisson process lets the intensity lambda(t) vary with time, so the expected number of faults found by time t, m(t) = ∫0^t lambda(s) ds, can rise quickly at first and then level off. This time-varying rate aligns with observed learning and debugging dynamics much better than a constant rate. The other ideas implied by the distractors—constant failure rate, no new faults, or failures never decreasing—don’t capture how reliability typically improves as more defects are addressed.