An Anatomy of 488 Faults from Defects4J Based on the Control- and Data-Flow Graph Representations of Programs
February 04, 2025 Β· Declared Dead Β· π International Conference on Evaluation & Assessment in Software Engineering
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Alexandra van der Spuy, Bernd Fischer
arXiv ID
2502.02299
Category
cs.SE: Software Engineering
Citations
0
Venue
International Conference on Evaluation & Assessment in Software Engineering
Last Checked
5 months ago
Abstract
Software fault datasets such as Defects4J provide for each individual fault its location and repair, but do not characterize the faults. Current classifications use the repairs as proxies, but these do not capture the intrinsic nature of the fault. In this paper, we propose a new, direct fault classification scheme based on the control- and data-flow graph representations of programs. Our scheme comprises six control-flow and two data-flow fault classes. We manually apply this scheme to 488 faults from seven projects in the Defects4J dataset. We find that the majority of the faults are assigned between one and three classes. We also find that one of the data-flow fault classes (definition fault) is the most common individual class but that the majority of faults are classified with at least one control-flow fault class. Our proposed classification can be applied to other fault datasets and can be used to improve fault localization and automated program repair techniques for specific fault classes.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Software Engineering
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Microservices: yesterday, today, and tomorrow
π
π
The Cartographer
A Survey of Machine Learning for Big Code and Naturalness
R.I.P.
π»
Ghosted
An Overview on Smart Contracts: Challenges, Advances and Platforms
R.I.P.
π»
Ghosted
Slither: A Static Analysis Framework For Smart Contracts
R.I.P.
π»
Ghosted
ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted