We present our tool, AndroFL, that provides an infrastructure for an evolutionary algorithm-based test-suite generation backed by a statistical fault localization module for diagnosing faults. AndroFL’s evolutionary test-generator supports configurable fitness functions (e.g., coverage, diagnosability metrics like Ulysis). The statistical fault localization engine supports popular metrics like Ochiai, Tarantula and Barinel, and allows adding custom fault localization metrics. We evaluated AndroFL on 20 open-sourced apps from F-Droid, and demonstrates significant efficiency gains: it reduces debugging effort by 74% (median EXAM score) compared to random testing—enabling developers to pinpoint faults ≈ 4× faster. Furthermore, AndroFL localizes 25% and 50% more faults compared to random testing in the top-5 and top-10 ranked list in worst case ranking scenario.