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Recent years have witnessed a growing focus on automated softwarevulnerability detection. Notably, deep learning (DL)-based methods, whichemploy source code for the implicit acquisition of vulnerability patterns, havedemonstrated superior performance compared to other approaches. However, theDL-based approaches are still hard to capture the vulnerability-relatedinformation from the whole code snippet, since the vulnerable parts usuallyaccount for only a small proportion. As evidenced by our experiments, theapproaches tend to excessively emphasize semantic information, potentiallyleading to limited vulnerability detection performance in practical scenarios.First, they cannot well distinguish between the code snippets before (i.e.,vulnerable code) and after (i.e., non-vulnerable code) developers' fixes due tothe minimal code changes. Besides, substituting user-defined identifiers withplaceholders (e.g., "VAR1" and "FUN1") in obvious performance degradation at upto 14.53% with respect to the F1 score. To mitigate these issues, we propose toleverage the vulnerable and corresponding fixed code snippets, in which theminimal changes can provide hints about semantic-agnostic features forvulnerability detection. In this paper, we propose a software vulneRabilitydEteCtion framework with zerO-sum game and prototype learNing, named RECON. InRECON, we propose a zero-sum game construction module. Distinguishing thevulnerable code from the corresponding fixed code is regarded as one player(i.e. Calibrator), while the conventional vulnerability detection is anotherplayer (i.e. Detector) in the zero-sum game. The goal is to capture thesemantic-agnostic features of the first player for enhancing the secondplayer's performance for vulnerability detection. Experiments on the publicbenchmark dataset show that RECON outperforms the state-of-the-art baseline by6.29% in F1 score.

Game Rewards Vulnerabilities: Software Vulnerability Detection with Zero-Sum Game and Prototype Learning