Is Timing Critical to Trace Reconstruction?

Dynamic analysis of real-world software systems is challenging due to imperfections, noise and data loss. Moreover, these systems evolve with time and their requirements are usually either not clearly specified or unknown, which makes it hard to analyze them. Therefore, it is important to create models that can learn to behave similarly to these systems to enable us to predict their actions, recover missing data, or detect potential failures ahead of time.Several models have been proposed to model sequential data, but the vast majority of them only have a qualitative notion of time or no notion of it at all. In this paper, we extend the work on incorporating a quantitative notion of time to RNN and introduce Time GRU. This modified GRU can learn the behaviour of complex software systems to a very high degree of accuracy. Our approach is scalable and has shown state-of-the-art performance on industry-strength software with real operating logs from Blackberry’s QNX real-time operating system. The proposed model can predict upcoming sequences of events more than 100 timesteps ahead in time with more than 90% accuracy. This allows for significant improvement in trace reconstruction and failure explainability.