  Explaining the prediction results of software defect prediction models is a
challenging while practical task, which can provide useful information for
developers to understand and fix the predicted bugs. To address this issue,
recently, Jiarpakdee et al. proposed to use {two state-of-the-art}
model-agnostic techniques (i.e., LIME and BreakDown) to explain the prediction
results of bug prediction models. Their experiments show these tools can
generate promising results and the generated explanations can assist developers
understand the prediction results. However, the fact that LIME and BreakDown
were only examined on a single software defect prediction model setting calls
into question about their consistency and reliability across software defect
prediction models with various settings.
  In this paper, we set out to investigate the consistency and reliability of
model-agnostic technique based explanation generation approaches (i.e., LIME
and BreakDown) on software defect prediction models with different settings ,
e.g., different data sampling techniques, different machine learning
classifiers, and different prediction scenarios. Specifically, we use both LIME
and BreakDown to generate explanations for the same instance under software
defect prediction models with different settings and then check the consistency
of the generated explanations for the instance. We reused the same defect data
from Jiarpakdee et al. in our experiments. The results show that both LIME and
BreakDown generate inconsistent explanations under different software defect
prediction settings for the same test instances, which makes them unreliable
for explanation generation. Overall, with this study, we call for more research
in explainable software defect prediction towards achieving consistent and
reliable explanation generation.
