Load Testing

    Research areas of interest include machine learning, data mining, game theory, and artificial intelligence as applied to solving performance and quality problems that are prevalent in the software development lifecycle. Load testing involves massive replication of simulated user load on a software system to simulate the stressors of a live production environment. Industry-standard load-testing software platforms can rely upon user data to generate test cases, however, such platforms only re-play or replicate the given user data. Our research focus is on extracting user behavior patterns from software log data to facilitate the automation of testing software systems. Providing an adaptive load-testing platform can improve the testing process by discovering novel behavioral patterns and emulating new test cases.

Chet Parrott Ph.D (C).

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