<p>In model-based software effort estimation (SEE), predictions are inherently sensitive to input perturbations due to incomplete and evolving requirements in early-stage development. Various enhancement strategies have addressed prediction error through bias mitigation and variance reduction; however, their effectiveness in addressing input perturbation robustness remains unexplored. This study evaluates input perturbation robustness as an enhancement strategy for linear programming for effort estimation (LP4EE), a relatively stable SEE baseline that exhibits low variance, and compares this approach against bias mitigation and variance reduction strategies. Four LP4EE variants were implemented: scenario-based robust optimization (SROpt) for input perturbation robustness, boosting for bias mitigation, and bagging and noise injection for variance reduction. These variants were benchmarked against the original LP4EE and eight machine learning methods across 14 datasets, using robust error measures and statistical tests. The SROpt variant outperformed all other evaluated methods, while the other three variants showed mixed or inferior performance compared with the original LP4EE. Addressing input perturbation robustness is more effective than conventional bias mitigation and variance reduction strategies for LP4EE, thereby establishing this strategy as a promising direction for model-based SEE approaches.</p>