A Fault Localization and Debugging Support Framework driven by Bug Tracking Data
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Fault localizationSimilar Papers 제목 키워드 기반
DeepFD: Automated Fault Diagnosis and Localization for Deep Learning Programs
As Deep Learning (DL) systems are widely deployed for mission-critical applications, debugging such systems becomes essential. Most existing works identify and repair suspicious neurons on the trained Deep Neural Network…
Deep LearningFault DiagnosisFault localizationLearning Tractable Probabilistic Models for Fault Localization
In recent years, several probabilistic techniques have been applied to various debugging problems. However, most existing probabilistic debugging systems use relatively simple statistical models, and fail to generalize a…
Fault localizationBug-Report-Driven Fault Localization: Industrial Benchmarking and Lesson Learned at ABB Robotics
Software quality assurance remains a major challenge in industrial environments, where large-scale and long-lived systems inevitably accumulate defects. Identifying the location of a fault is often time-consuming and cos…
Text ClassificationData AugmentationFedDebug: Systematic Debugging for Federated Learning Applications
In Federated Learning (FL), clients independently train local models and share them with a central aggregator to build a global model. Impermissibility to access clients' data and collaborative training make FL appealing…
Fault localizationFederated LearningCFaults: Model-Based Diagnosis for Fault Localization in C Programs with Multiple Test Cases
Debugging is one of the most time-consuming and expensive tasks in software development. Several formula-based fault localization (FBFL) methods have been proposed, but they fail to guarantee a set of diagnoses across al…
Fault localization