  Fault localization has been determined as a major resource factor in the
software development life cycle. Academic fault localization techniques are
mostly unknown and unused in professional environments. Although manual
debugging approaches can vary significantly depending on bug type (e.g. memory
bugs or semantic bugs), these differences are not reflected in most existing
fault localization tools. Little research has gone into automated
identification of bug types to optimize the fault localization process.
Further, existing fault localization techniques leverage on historical data
only for augmentation of suspiciousness rankings. This thesis aims to provide a
fault localization framework by combining data from various sources to help
developers in the fault localization process. To achieve this, a bug
classification schema is introduced, benchmarks are created, and a novel fault
localization method based on historical data is proposed.
