In this paper, we derive sufficiency results on the number of group tests required, in a non-adaptive random pooling matrix setting, to find almost all the defective and non-defective items with high confidence, via two popular algorithms in the group testing literature, namely CoMa and DD. To this end, we propose viewing the group testing problem as an online function learning problem and develop our analysis using the probably approximately correct (PAC) framework. We compare the derived bounds with existing bounds literature for exact recovery both theoretically and using simulations. We also illustrate the savings in the number of tests required for approximate defective set recovery compared to exact recovery.