The theoretical developments of data-driven fault diagnosis methods have yielded fruitful
achievements and significantly benefited industry practices. However, most methods are …

Researchers have recently achieved significant advances in deep learning techniques,
which in turn has substantially advanced other research disciplines, such as natural …

Numerous fault localization techniques identify suspicious statements potentially
responsible for program failures by discovering the statistical correlation between test results …

Enlightened by the big success of pre-training in natural language processing, pre-trained
models for programming languages have been widely used to promote code intelligence in …

Software developers spend a significant portion of time fixing bugs in their projects. To
streamline this process, bug localization approaches have been proposed to identify the …

Automated debugging is an emerging research field that aims to automatically find and
repair bugs. In this field, Fault Localization (FL) and Automated Program Repair (APR) gain …

Data-driven intelligent methods have demonstrated their effectiveness in the area of fault
diagnosis. However, most existing studies are based on the assumption that the distributions …

Effort‐aware cross‐project defect prediction (EACPDP), which uses cross‐project software
modules to build a model to rank within‐project software modules based on the defect …

Context: Fault localization (FL) is essentially a search over the space of program statements
to find suspicious entities that might have caused a program failure. However, the input data …

Fault localization (FL) analyzes the execution information of a test suite to pinpoint the root
cause of a failure. The class imbalance of a test suite, ie, the imbalanced class proportion …