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

arXiv: https://arxiv.org/abs/2103.02386 · HTML: https://mlsanity.com/paper/2103.02386/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 42 references that are in the map.
- First posted on arXiv 3 Mar 2021.

## Builds on (most cited first, 2 of 2)

- [TBar: Revisiting Template-based Automated Program Repair](https://mlsanity.com/paper/1903.08409/) (2019)
- [Towards an automated approach for bug fix pattern detection](https://mlsanity.com/paper/1807.11286/) (2018)

## Built on by (most cited first, 1 of 1)

- [Where's the Bug? Attention Probing for Scalable Fault Localization](https://mlsanity.com/paper/2502.13966/) (2025)

## Abstract (arXiv)

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.