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Load tests ensure that software systems are able to perform under the expected workloads. The current state of load test analysis requires significant manual review of performance counters and execution logs, and a high degree of system-specific expertise. In particular, memory-related issues (e.g., memory leaks or spikes), which may degrade performance and cause crashes, are difficult to diagnose. Performance analysts must correlate hundreds of megabytes or gigabytes of performance counters (to understand resource usage) with execution logs (to understand system behaviour). However, little work has been done to combine these two types of information to assist performance analysts in their diagnosis. We propose an automated approach that combines performance counters and execution logs to diagnose memory-related issues in load tests. We perform three case studies on two systems: one open-source system and one large-scale enterprise system. Our approach flags ≤ 0.1% of the execution logs with a precision ≥ 80%.