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

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

Informationsteknologi

Forskningsoutput: Kapitel i bok/konferenshandling › Publicerad konferensartikel › Vetenskaplig › Peer review

13 Nedladdningar (Pure)

Sammanfattning

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.

Originalspråk	Engelska
Titel på värdpublikation	2025 IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2025
Redaktörer	Anna Rita Fasolino, Sebastiano Panichella, Aldeida Aleti, Ali Mesbah
Förlag	IEEE
Sidor	217-225
ISBN (elektroniskt)	979-8-3315-3467-7
ISBN (tryckt)	979-8-3315-3468-4
DOI	https://doi.org/10.1109/ICSTW64639.2025.10962473
Status	Publicerad - 16 apr. 2025
MoE-publikationstyp	A4 Artikel i en konferenspublikation
Evenemang	IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW) - Varaktighet: 31 mars 2025 → …

Publikationsserier

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

Konferens

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

Finansiering

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.

FN:s SDG:er

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Åtkomst av dokument

10.1109/ICSTW64639.2025.10962473

VST: Virtual Sea Trial
Truscan, D. (Ansvarig forskare), Hellström, M. (Ansvarig forskare), Porres Paltor, I. (CoPI), Ahmad, T. (CoI), Chariyarupadannayil Sudheerbabu, G. (Projektmedarbetare), Yaseen, A. (Projektmedarbetare), Khan, S. (Projektmedarbetare) & Mughees, A. (Projektmedarbetare)
Business Finland
01/01/24 → 31/12/26
Projekt: Företag/Business Finland

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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. I A. R. Fasolino, S. Panichella, A. Aleti, & A. Mesbah (Red.), 2025 IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2025 (s. 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. redaktör / Anna Rita Fasolino ; Sebastiano Panichella ; Aldeida Aleti ; Ali Mesbah. IEEE, 2025. s. 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",

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

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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. i AR Fasolino, S Panichella, A Aleti & A Mesbah (red), 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, s. 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. red. / Anna Rita Fasolino; Sebastiano Panichella; Aldeida Aleti; Ali Mesbah. IEEE, 2025. s. 217-225 (2025 IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2025).

Forskningsoutput: Kapitel i bok/konferenshandling › Publicerad konferensartikel › Vetenskaplig › 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

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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.

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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. I Fasolino AR, Panichella S, Aleti A, Mesbah A, redaktörer, 2025 IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2025. IEEE. 2025. s. 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

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