Conventional concolic testing has been used to provide high coverage of paths in statically typed languages. While it has also been applied in the context of JavaScript (JS) programs, we observe that applying concolic testing to dynamically-typed JS programs involves tackling unique problems to ensure scalability. In particular, a naive type-agnostic extension of concolic testing to JS programs causes generation of large number of inputs. Consequently, many executions operate on undefined values and repeatedly explore same paths resulting in redundant tests, thus diminishing the scalability of testing drastically.

Redundant traversal of loops in the context of other loops has been recently identified as a source of performance bugs in many Java libraries. This has resulted in the design of static and dynamic analysis techniques to detect these performance bugs automatically. However, while the effectiveness of dynamic analyses is dependent on the analyzed input tests, static analyses are less effective in automatically validating the presence of these problems, validating the fixes and avoiding regressions in future versions. This necessitates the design of an approach to automatically generate tests for exposing redundant traversal of loops.

Deterministic multithreaded systems (DMTs) are designed to ensure reproducibility of program behavior for a given input. In these systems, even minor changes to the code (or input) can perturb the schedule. This increases the number of feasible schedules making reasoning about these programs harder. Stable multithreaded systems (StableMTs) address the problem such that a schedule is unaffected by minor changes. Unfortunately, determinism in these systems can potentially serialize the execution imposing a significant penalty on the performance. Programmer hints in the form of soft barriers attempt to eliminate the performance bottlenecks. However, the process is arduous, error-prone and requires manual intervention to reconsider the location of the barrier for every modification to the source code. In this paper, we propose an effective approach to automate the task of adding soft barriers in the source code. Our approach analyzes the deterministic program executions to extract the program and semantic order of executions and builds a weighted constraint graph. Using this graph, a schedule is synthesized which is used to identify bottlenecks and insert soft barriers in the program source. We validate our implementation, named PEGASUS, by applying it on multiple benchmarks. Our experimental results demonstrate that we are able to reduce the overall execution time of programs by upto 34% when compared to the execution time where barriers are inserted manually. Moreover, we observe a performance improvement ranging from 38% to 88% as compared to programs without barriers. Our experimental results show that adapting PEGASUS to infer barriers for multiple versions of a source program is seamless. The memory and time overheads associated with the usage of PEGASUS is negligible making it a vital cog in building scalable StableMTs.