  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.
