Visual Spectrum-Based Fault Localization for Python Programs Based on the Differentiation of Execution Slices

Shehroz Khan; Gaadha Sudheerbabu; Bianca Elena Staicu; Tanwir Ahmad; Dragos Truscan

doi:10.1109/ICSTW64639.2025.10962473

Visual Spectrum-Based Fault Localization for Python Programs Based on the Differentiation of Execution Slices

Information Technology

Research output: Chapter in Book/Conference proceeding › Published conference proceeding › Scientific › peer-review

13 Downloads (Pure)

Abstract

We present an automated fault localization technique that can assist developers to localize effectively faults in Python programs. The proposed method uses spectrum-based fault localization techniques, program slicing, and graph-based visualization to formulate an efficient method for reducing the effort needed in fault localization. The approach takes the source code of a program, a set of passed and failed tests and collects the program spectra information by executing the tests. A tool, FaultLocalizer, facilitates the generation of a call graph for inter-procedural dependency analysis and annotated control flow graphs for different modules with spectra information and suspiciousness scores. The focus of the approach is on the visual analysis of the source code, and it is intended to complement existing fault localization approaches. The effectiveness of the proposed approach is evaluated on a set of buggy Python programs. The results show that the approach reduces debugging efforts and can be applied to programs with conditional branching.

Original language	English
Title of host publication	2025 IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2025
Editors	Anna Rita Fasolino, Sebastiano Panichella, Aldeida Aleti, Ali Mesbah
Publisher	IEEE
Pages	217-225
ISBN (Electronic)	979-8-3315-3467-7
ISBN (Print)	979-8-3315-3468-4
DOIs	https://doi.org/10.1109/ICSTW64639.2025.10962473
Publication status	Published - 16 Apr 2025
MoE publication type	A4 Article in a conference publication
Event	IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW) - Duration: 31 Mar 2025 → …

Publication series

Name	2025 IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2025

Conference

Conference	IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)
Period	31/03/25 → …

Funding

This work was partially funded by the the European Union's Horizon 2020 research and innovation programme, under grant agreement No. 957212 (VeriDevOps) and by Business Finland via the Virtual Sea Trial project (VST), under grant 7187/31/2023.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

Spectrum-based fault localization
program slicing
program spectra

Access to Document

10.1109/ICSTW64639.2025.10962473

VST: Virtual Sea Trial
Truscan, D. (Principal Investigator), Hellström, M. (Principal Investigator), Porres Paltor, I. (Co-Principal Investigator), Ahmad, T. (Co-Investigator), Chariyarupadannayil Sudheerbabu, G. (Project staff), Yaseen, A. (Project staff), Khan, S. (Project staff), Mughees, A. (Project staff) & Kulshreshtha, A. (Project staff)
Business Finland
01/01/24 → 31/12/26
Project: Industry/Business Finland

Cite this

Khan, S., Sudheerbabu, G., Elena Staicu, B., Ahmad, T., & Truscan, D. (2025). Visual Spectrum-Based Fault Localization for Python Programs Based on the Differentiation of Execution Slices. In A. R. Fasolino, S. Panichella, A. Aleti, & A. Mesbah (Eds.), 2025 IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2025 (pp. 217-225). (2025 IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2025). IEEE. https://doi.org/10.1109/ICSTW64639.2025.10962473

Khan, Shehroz ; Sudheerbabu, Gaadha ; Elena Staicu, Bianca et al. / Visual Spectrum-Based Fault Localization for Python Programs Based on the Differentiation of Execution Slices. 2025 IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2025. editor / Anna Rita Fasolino ; Sebastiano Panichella ; Aldeida Aleti ; Ali Mesbah. IEEE, 2025. pp. 217-225 (2025 IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2025).

@inproceedings{aab186353eef4b29b81c598fc9b727c0,

title = "Visual Spectrum-Based Fault Localization for Python Programs Based on the Differentiation of Execution Slices",

abstract = "We present an automated fault localization technique that can assist developers to localize effectively faults in Python programs. The proposed method uses spectrum-based fault localization techniques, program slicing, and graph-based visualization to formulate an efficient method for reducing the effort needed in fault localization. The approach takes the source code of a program, a set of passed and failed tests and collects the program spectra information by executing the tests. A tool, FaultLocalizer, facilitates the generation of a call graph for inter-procedural dependency analysis and annotated control flow graphs for different modules with spectra information and suspiciousness scores. The focus of the approach is on the visual analysis of the source code, and it is intended to complement existing fault localization approaches. The effectiveness of the proposed approach is evaluated on a set of buggy Python programs. The results show that the approach reduces debugging efforts and can be applied to programs with conditional branching.",

keywords = "Spectrum-based fault localization, program slicing, program spectra",

author = "Shehroz Khan and Gaadha Sudheerbabu and \{Elena Staicu\}, Bianca and Tanwir Ahmad and Dragos Truscan",

year = "2025",

month = apr,

day = "16",

doi = "10.1109/ICSTW64639.2025.10962473",

language = "English",

isbn = "979-8-3315-3468-4",

series = "2025 IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2025",

publisher = "IEEE",

pages = "217--225",

editor = "Fasolino, \{Anna Rita\} and Sebastiano Panichella and Aldeida Aleti and Ali Mesbah",

booktitle = "2025 IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2025",

address = "United States",

note = "IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW) ; Conference date: 31-03-2025",

}

Khan, S , Sudheerbabu, G, Elena Staicu, B, Ahmad, T & Truscan, D 2025, Visual Spectrum-Based Fault Localization for Python Programs Based on the Differentiation of Execution Slices. in AR Fasolino, S Panichella, A Aleti & A Mesbah (eds), 2025 IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2025. 2025 IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2025, IEEE, pp. 217-225, IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW), 31/03/25. https://doi.org/10.1109/ICSTW64639.2025.10962473

Visual Spectrum-Based Fault Localization for Python Programs Based on the Differentiation of Execution Slices. / Khan, Shehroz ; Sudheerbabu, Gaadha; Elena Staicu, Bianca et al.
2025 IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2025. ed. / Anna Rita Fasolino; Sebastiano Panichella; Aldeida Aleti; Ali Mesbah. IEEE, 2025. p. 217-225 (2025 IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2025).

Research output: Chapter in Book/Conference proceeding › Published conference proceeding › Scientific › peer-review

TY - GEN

T1 - Visual Spectrum-Based Fault Localization for Python Programs Based on the Differentiation of Execution Slices

AU - Khan, Shehroz

AU - Sudheerbabu, Gaadha

AU - Elena Staicu, Bianca

AU - Ahmad, Tanwir

AU - Truscan, Dragos

PY - 2025/4/16

Y1 - 2025/4/16

N2 - We present an automated fault localization technique that can assist developers to localize effectively faults in Python programs. The proposed method uses spectrum-based fault localization techniques, program slicing, and graph-based visualization to formulate an efficient method for reducing the effort needed in fault localization. The approach takes the source code of a program, a set of passed and failed tests and collects the program spectra information by executing the tests. A tool, FaultLocalizer, facilitates the generation of a call graph for inter-procedural dependency analysis and annotated control flow graphs for different modules with spectra information and suspiciousness scores. The focus of the approach is on the visual analysis of the source code, and it is intended to complement existing fault localization approaches. The effectiveness of the proposed approach is evaluated on a set of buggy Python programs. The results show that the approach reduces debugging efforts and can be applied to programs with conditional branching.

AB - We present an automated fault localization technique that can assist developers to localize effectively faults in Python programs. The proposed method uses spectrum-based fault localization techniques, program slicing, and graph-based visualization to formulate an efficient method for reducing the effort needed in fault localization. The approach takes the source code of a program, a set of passed and failed tests and collects the program spectra information by executing the tests. A tool, FaultLocalizer, facilitates the generation of a call graph for inter-procedural dependency analysis and annotated control flow graphs for different modules with spectra information and suspiciousness scores. The focus of the approach is on the visual analysis of the source code, and it is intended to complement existing fault localization approaches. The effectiveness of the proposed approach is evaluated on a set of buggy Python programs. The results show that the approach reduces debugging efforts and can be applied to programs with conditional branching.

KW - Spectrum-based fault localization

KW - program slicing

KW - program spectra

U2 - 10.1109/ICSTW64639.2025.10962473

DO - 10.1109/ICSTW64639.2025.10962473

M3 - Published conference proceeding

SN - 979-8-3315-3468-4

T3 - 2025 IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2025

SP - 217

EP - 225

BT - 2025 IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2025

A2 - Fasolino, Anna Rita

A2 - Panichella, Sebastiano

A2 - Aleti, Aldeida

A2 - Mesbah, Ali

PB - IEEE

T2 - IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)

Y2 - 31 March 2025

ER -

Khan S , Sudheerbabu G, Elena Staicu B, Ahmad T , Truscan D. Visual Spectrum-Based Fault Localization for Python Programs Based on the Differentiation of Execution Slices. In Fasolino AR, Panichella S, Aleti A, Mesbah A, editors, 2025 IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2025. IEEE. 2025. p. 217-225. (2025 IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2025). doi: 10.1109/ICSTW64639.2025.10962473

Visual Spectrum-Based Fault Localization for Python Programs Based on the Differentiation of Execution Slices

Abstract

Publication series

Conference

Funding

UN SDGs

Keywords

Access to Document

Fingerprint

Projects

VST: Virtual Sea Trial

Cite this