With the rise of autonomous systems, the automation of faults detection and localization becomes critical totheir reliability. An automated strategy that can provide a ranked list of faulty modules or files with respect tohow likely they contain the root cause of the problem would help in the automation bug localization. Learningfrom the history if previously located bugs in general, and extracting the dependencies between these bugs inparticular, helps in building models to accurately localize any potentially detected bugs. In this study, we proposea novel fault localization solution based on a learning-to-rank strategy, using the history of previously localizedbugs and their dependencies as features, to rank files in terms of their likelihood of being a root cause of a bug.The evaluation of our approach has shown its efficiency in localizing dependent bugs.
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