A Fault Localization and Debugging Support Framework driven by Bug Tracking Data

TL;DR

This paper presents a comprehensive fault localization framework that integrates bug tracking data, introduces a bug classification schema, and proposes a novel method leveraging historical data to improve debugging efficiency.

Contribution

It introduces a new fault localization framework combining multiple data sources, a bug classification schema, and a novel historical-data-based localization method.

Findings

Created benchmarks for fault localization evaluation

Developed a bug classification schema for different bug types

Proposed a new fault localization method using historical data

Abstract

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.