  The rapid escalation of applying Machine Learning (ML) in various domains has
led to paying more attention to the quality of ML components. There is then a
growth of techniques and tools aiming at improving the quality of ML components
and integrating them into the ML-based system safely. Although most of these
tools use bugs' lifecycle, there is no standard benchmark of bugs to assess
their performance, compare them and discuss their advantages and weaknesses. In
this study, we firstly investigate the reproducibility and verifiability of the
bugs in ML-based systems and show the most important factors in each one. Then,
we explore the challenges of generating a benchmark of bugs in ML-based
software systems and provide a bug benchmark namely defect4ML that satisfies
all criteria of standard benchmark, i.e. relevance, reproducibility, fairness,
verifiability, and usability. This faultload benchmark contains 100 bugs
reported by ML developers in GitHub and Stack Overflow, using two of the most
popular ML frameworks: TensorFlow and Keras. defect4ML also addresses important
challenges in Software Reliability Engineering of ML-based software systems,
like: 1) fast changes in frameworks, by providing various bugs for different
versions of frameworks, 2) code portability, by delivering similar bugs in
different ML frameworks, 3) bug reproducibility, by providing fully
reproducible bugs with complete information about required dependencies and
data, and 4) lack of detailed information on bugs, by presenting links to the
bugs' origins. defect4ML can be of interest to ML-based systems practitioners
and researchers to assess their testing tools and techniques.
