MatchFixAgent: Language-Agnostic Autonomous Repository-Level Code Translation Validation and Repair

Code translation transforms source code from one programming language (PL) to another. Validating the functional equivalence of translation and repairing, if necessary, are critical steps in code translation. Existing automated validation and repair approaches struggle to generalize to many PLs due to high engineering overhead, and they rely on existing and often inadequate test suites, which results in false claims of equivalence and ineffective translation repair. We develop MatchFixAgent, a large language model (LLM)-based, PL-agnostic framework for equivalence validation and repair of translations. MatchFixAgent features a multi-agent architecture that divides equivalence validation into several sub-tasks to ensure thorough and consistent semantic analysis of the translation. Then it feeds this analysis to test agent to write and execute tests. Upon observing a test failure, the repair agent attempts to fix the translation bug. The final (in)equivalence decision is made by the verdict agent, considering semantic analyses and test execution results. We compare MatchFixAgent's validation and repair results with four repository-level code translation techniques. We use 2,219 translation pairs from their artifacts, which cover 6 PL pairs, and are collected from 24 GitHub projects totaling over 900K lines of code. Our results demonstrate that MatchFixAgent produces (in)equivalence verdicts for 99.2% of translation pairs, with the same equivalence validation result as prior work on 72.8% of them. When MatchFixAgent's result disagrees with prior work, we find that 60.7% of the time MatchFixAgent's result is actually correct. In addition, we show that MatchFixAgent can repair 50.6% of inequivalent translation, compared to prior work's 18.5%. This demonstrates that MatchFixAgent is far more adaptable to many PL pairs than prior work, while producing highly accurate validation results.

Hypergraph-Guided Regex Filter Synthesis for Event-Based Anomaly Detection

We propose HyGLAD, a novel algorithm that automatically builds a set of interpretable patterns that model event data. These patterns can then be used to detect event-based anomalies in a stationary system, where any deviation from past behavior may indicate malicious activity. The algorithm infers equivalence classes of entities with similar behavior observed from the events, and then builds regular expressions that capture the values of those entities. As opposed to deep-learning approaches, the regular expressions are directly interpretable, which also translates to interpretable anomalies. We evaluate HyGLAD against all 7 unsupervised anomaly detection methods from DeepOD on five datasets from real-world systems. The experimental results show that on average HyGLAD outperforms existing deep-learning methods while being an order of magnitude more efficient in training and inference (single CPU vs GPU). Precision improved by 1.2x and recall by 1.3x compared to the second-best baseline.