  With the recent advancements in machine learning (ML), numerous ML-based
approaches have been extensively applied in software analytics tasks to
streamline software development and maintenance processes. Nevertheless,
studies indicate that despite their potential usefulness, ML models are
vulnerable to adversarial attacks, which may result in significant monetary
losses in these processes. As a result, the ML models' robustness against
adversarial attacks must be assessed before they are deployed in software
analytics tasks. Despite several techniques being available for adversarial
attacks in software analytics tasks, exploring adversarial attacks using ML
explainability is largely unexplored. Therefore, this study aims to investigate
the relationship between ML explainability and adversarial attacks to measure
the robustness of ML models in software analytics tasks. In addition, unlike
most existing attacks that directly perturb input-space, our attack approach
focuses on perturbing feature-space. Our extensive experiments, involving six
datasets, three ML explainability techniques, and seven ML models, demonstrate
that ML explainability can be used to conduct successful adversarial attacks on
ML models in software analytics tasks. This is achieved by modifying only the
top 1-3 important features identified by ML explainability techniques.
Consequently, the ML models under attack fail to accurately predict up to 86.6%
of instances that were correctly predicted before adversarial attacks,
indicating the models' low robustness against such attacks. Finally, our
proposed technique demonstrates promising results compared to four
state-of-the-art adversarial attack techniques targeting tabular data.
