Disruptions in tokamak devices are inevitable and can severely damage the device's wall. For this reason, different protection mechanisms have to be implemented. In Join European Torus (JET), these protection systems are structured in different levels. At the lowest level are those systems that are responsible for protecting the machine's integrity, which must be highly reliable. More complex systems are located at higher levels; these higher-level systems have been designed to take action before low-level systems. Since the installation of the new metallic wall in JET, new protection systems have been being developed to improve the overall protection of the device. This work focuses on a software application ­ a disruption predictor ­ that detects an incoming disruption. This software application simulates the behavior of a real-time implementation. In recent years, efforts have been devoted to developing and optimizing a reliable system that is capable of predicting disruptions. This has been accomplished by the novel combination of machine learning techniques based on supervised learning methods. Disruptions must be predicted early enough so that the protection systems can mitigate the effects of disruptions. This article summarizes the software development of the JET disruption predictor. This software simulates the real-time data acquisition and data processing. It has been an essential software tool to both optimize the disruption prediction model and implement a simulator of the real-time predictor.