Generation of Natural-Language Explanations for Static-Analysis Warnings Using Single- and Multi-Objective Optimization

Explanations for static-analysis warnings assist developers in understanding potential code issues. An end-to-end pipeline was implemented to generate natural-language explanations, evaluated on 5183 warning–explanation pairs from Java repositories, including a manually validated gold subset of 1176 examples for faithfulness assessment. Explanations were produced by a transformer-based encoder–decoder model (CodeT5) conditioned on warning types, contextual code snippets, and static-analysis evidence. Initial experiments employed single-objective optimization for hyperparameters (using a genetic algorithm with dynamic search-space correction, which adaptively adjusted search bounds based on the evolving distribution of candidate solutions, clustering promising regions, and pruning unproductive ones), but this approach enforced a fixed faithfulness–fluency trade-off; therefore, a multi-objective evolutionary algorithm (NSGA-II) was adopted to jointly optimize both criteria. Pareto-optimal configurations improved normalized faithfulness by up to 12% and textual quality by 5–8% compared to baseline CodeT5 settings, with batch sizes of 10–21, learning rates 2.3×10−5 to 5×10−4, maximum token lengths of 36–65, beam width 5, length penalty 1.15, and nucleus sampling p=0.88. Candidate explanations were reranked using a composite score of likelihood, faithfulness, and code-usefulness, producing final outputs in under 0.001 s per example. The results indicate that structured conditioning, evolutionary hyperparameter search, and reranking yield explanations that are both aligned with static-analysis evidence and linguistically coherent.