The memory allocator can have an important impact in symbolic execution. Taking a user-centric view, this tool demonstration paper discusses some of the main benefits provided by KLEE's new allocator KDAlloc in terms of improved deterministic execution and bug-finding capabilities. We then introduce a new replay tool for KLEE which enables the native execution to integrate KDAlloc and receive the same heap addresses as during symbolic execution.

This paper reports on our experience implementing a technique for sifting through static analysis reports using dynamic symbolic execution. Our insight is that if a static analysis tool produces a partial trace through the program under analysis, annotated with conditions that the analyser believes are important for the bug to trigger, then a dynamic symbolic execution tool may be able to exploit the trace by (a) guiding the search heuristically so that paths that follow the trace most closely are prioritised for exploration, and (b) pruning the search using the conditions associated with each step of the trace. This may allow the bug to be quickly confirmed using dynamic symbolic execution, if it turns out to be a true positive, yielding an input that triggers the bug.

When symbolic execution is used to analyse real-world applications, it often consumes all available memory in a relatively short amount of time, sometimes making it impossible to analyse an application for an extended period. In this paper, we present a technique that can record an ongoing symbolic execution analysis to disk and selectively restore paths of interest later, making it possible to run symbolic execution indefinitely. To be successful, our approach addresses several essential research challenges related to detecting divergences on re-execution, storing long-running executions efficiently, changing search heuristics during re-execution, and providing a global view of the stored execution. Our extensive evaluation of 93 Linux applications shows that our approach is practical, enabling these applications to run for days while continuing to explore new execution paths.

Symbolic execution is a well established technique for software testing and analysis. However, scalability continues to be a challenge, both in terms of constraint solving cost and path explosion. In this work, we present a novel approach for symbolic execution, which can enhance its scalability by aggressively prioritising execution paths that are already known to be feasible, and deferring all other paths. We evaluate our technique on nine applications, including SQLite3, make and tcpdump and show it can achieve higher coverage for both seeded and non-seeded exploration.