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Previous studies have demonstrated that neural code comprehension models arevulnerable to identifier naming. By renaming as few as one identifier in thesource code, the models would output completely irrelevant results, indicatingthat identifiers can be misleading for model prediction. However, identifiersare not completely detrimental to code comprehension, since the semantics ofidentifier names can be related to the program semantics. Well exploiting thetwo opposite impacts of identifiers is essential for enhancing the robustnessand accuracy of neural code comprehension, and still remains under-explored. Inthis work, we propose to model the impact of identifiers from a novel causalperspective, and propose a counterfactual reasoning-based framework namedCREAM. CREAM explicitly captures the misleading information of identifiersthrough multi-task learning in the training stage, and reduces the misleadingimpact by counterfactual inference in the inference stage. We evaluate CREAM onthree popular neural code comprehension tasks, including function naming,defect detection and code classification. Experiment results show that CREAMnot only significantly outperforms baselines in terms of robustness (e.g.,+37.9% on the function naming task at F1 score), but also achieve improvedresults on the original datasets (e.g., +0.5% on the function naming task at F1score).

Two Sides of the Same Coin: Exploiting the Impact of Identifiers in Neural Code Comprehension