Today's real-world software systems are often too complex to reason about formally, which can cause expensive failures which could be avoided with improved analysis in the process of their creation. We here seek to demonstrate that heuristic methods can improve the techniques used to enable and enhance explainability and reasoning for these systems, such as symbolic execution and model checking, thus making the systems they support easier to design, develop, and debug. To this end, we propose a set of new tools for a diverse set of traditionally difficult-to-analyze systems, including neural networks and symbolic execution engines. These tools and techniques use approximation-based insights to show the power of this idea. Experimental evaluation shows that these techniques and tools can improve both explanability and analyzability.Electrical and Computer Engineerin
