Abstract: Testing with manually generated test cases is the primary technique used in industry to improve reliability of software--in fact, such testing is reported to account for over half of the typical cost of software development. I will describe Concolic Testing, a systematic and efficient method which combines random and symbolic testing. Concolic testing enables automatic and systematic testing of large programs, avoids redundant test cases and does not generate false warnings. Experiments on real-world software show that concolic testing can be used to effectively catch generic errors such as assertion violations, memory leaks, uncaught exceptions, and segmentation faults. Combined with dynamic partial order reduction techniques and predictive analysis, concolic testing is effective in catching concurrency bugs such as data races and deadlocks as well as specification related bugs. I will describe my experience with building two concolic testing tools, CUTE for C programs and jCUTE for Java programs, and applying these tools to real-world software systems. Finally, I will provide a brief overview of my research in statistical and probabilistic model checking, application of machine learning to verify infinite state systems, runtime monitoring of distributed and concurrent systems, and probabilistic programming.
