An engineered product is said to experience reliability growth when (1) a failure mode is observed and investigated until the root cause is identified and (2) an understanding of the root cause failure mechanism is then developed and used to formulate and implement a corrective action that reduces or eliminates the probability or frequency of the failure mode's recurrence within the product population. This article addresses how one identifies failure modes during developmental product testing, develops an understanding of them, and formulates corrective actions to mitigate them. A brief introduction to statistical reliability‐growth modeling is provided, along with lessons learned from its application to complex repairable assemblies and products. Statistical reliability‐growth modeling was developed to pool data across evolving product configurations as failure modes are observed and mitigated during a sequence of developmental tests. In most applications, one does not have sufficient data to generate final configuration estimates of satisfactory uncertainty; the pooling of data from current and prior configurations, using a method that accounts for the impact of corrective actions, can provide a clearer understanding of reliability growth and support improved product development decision‐making (e.g., when to grant production approval). The most crucial lesson learned from several decades of applications is that successful reliability‐growth programs require that all but the most elusive failure modes be identified and mitigated prior to product‐level reliability‐growth testing; this key engineering activity enables the product to meet the high level of initial reliability required for success.