In software debugging, fault localization is the most difficult, expensive, tedious, and time-
consuming task, particularly for large-scale software systems. This is due to the fact that it …

Data is the fuel to models, and it is still applicable in fault localization (FL). Many existing
elaborate FL techniques take the code coverage matrix and failure vector as inputs …

Context: The recent progress of deep learning has shown its promising learning ability in
making sense of data, and many fields have utilized this learning ability to learn an effective …

Statistical fault localization is an easily deployed technique for quickly determining
candidates for faulty code locations. If a human programmer has to search the fault beyond …

Context: In the field of software engineering, the most complex and time consuming activity
is fault-finding. Due to increasing size and complexity of software, there is a necessity of …

Identifying the location of faults in programs has been recognized as one of the most
manually and time cost activities during software debugging process. Fault localization …

Trace data is critical for fault localization (FL) to analyze suspicious statements potentially
responsible for a failure. However, existing trace representation meets its bottleneck mainly …

Modern automated program repair (APR) is well-tuned to finding and repairing bugs that
introduce observable erroneous behavior to a program. However, a significant class of bugs …

Spectrum-based fault localization (SBFL) works well for single-fault programs but its
accuracy decays for increasing fault numbers. We present FLITSR (Fault Localization by …

Abstract In traditional Spectrum-Based Fault Localization (SBFL), hit-based spectrum is used
to estimate a program element's suspiciousness to contain a fault, ie, only the binary …