Automated Crash Reproduction (ACR) is an area of software testing research that aims to reproduce software crashes to improve developers' ability to debug programs. There has been little progress in applying ACR techniques to JavaScript, as the highly dynamic nature of JavaScript poses challenges for program analysis and synthesis. We present SynTest-ACR, the first tool for ACR in JavaScript, applying artificial intelligence techniques to evolve suitable reproduction cases. We have evaluated SynTest-ACR against the CrashJS dataset consisting of 453 crashes. As a baseline, we ported the state-of-the-art searchguiding fitness function from EvoCrash for Java, finding that it performs much worse when applied to JavaScript programs, and through comprehensively designing and evaluating alternative fitness functions more suitable for JS ACR we obtain an 18.9% increase in reproduction rate over this baseline for Syntest-ACR.

Software bugs often lead to software crashes, which cost US companies upwards of $2.08 trillion annually. Automated Crash Reproduction (ACR) aims to generate unit tests that successfully reproduce a crash. The goal of ACR is to aid developers with debugging, providing them with another tool to locate where a bug is in a program. The main approach ACR currently takes is to replicate a stack trace from an error thrown within a program. Currently, ACR has been developed for C, Java, and Python, but there are no tools targeting JavaScript programs. To aid the development of JavaScript ACR tools, we propose CrashJS: a benchmark dataset of 453 Node.js crashes from several sources. CrashJS includes a mix of real-world and synthesised tests, multiple projects, and different levels of complexity for both crashes and target programs.

This paper is a partial reproduction of work by Soltani et al. which presented EvoCrash, a tool for replicating software failures in Java by reproducing stack traces. EvoCrash uses a guided genetic algorithm to generate JUnit test cases capable of reproducing failures more reliably than existing coverage-based solutions. In this paper, we present the findings of our reproduction of the initial study exploring the effectiveness of EvoCrash and comparison to three existing solutions: STAR, JCHARMING, and MuCrash. We further explored the capabilities of EvoCrash on different programs to check for selection bias. We found that we can reproduce the crashes covered by EvoCrash in the original study while reproducing two additional crashes not reported as reproduced. We also find that EvoCrash was unsuccessful in reproducing several crashes from the JCHARMING paper, which were excluded from the original study. Both EvoCrash and JCHARMING could reproduce 73% of the crashes from the JCHARMING paper. We found that there was potentially some selection bias in the dataset for EvoCrash. We also found that some crashes had been reported as non-reproducible even when EvoCrash could reproduce them. We suggest this may be due to EvoCrash becoming stuck in a local optimum.