Spectrum-based fault localization for context-free grammars

Raselimo, Moeketsi; Fischer, Bernd

doi:10.1145/3357766.3359538

Cited by 10 publications

(1 citation statement)

References 45 publications

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“…Our evaluation uses simple program execution spectra derived from Java-based systems. Spectra derived from other languages [3,32,48,56,58,59] or involving more complex types of elements (e.g., data flow edges [49]) could have different characteristics that impact the results.…”

Section: Threats To Validitymentioning

confidence: 99%

Improving Spectrum-Based Localization of Multiple Faults by Iterative Test Suite Reduction

Callaghan,

Fischer

2023

Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis

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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 Iterative Test Suite Reduction), a novel SBFL extension that improves the localization of a given base metric specifically in the presence of multiple faults. FLITSR iteratively selects reduced versions of the test suite that better localize the individual faults in the system. This allows it to identify and re-rank faults ranked too low by the base metric because they were masked by other program elements.We evaluated FLITSR over method-level spectra from an existing large synthetic dataset comprising 75000 variants of 15 opensource projects with up to 32 injected faults, as well as method-and statement-level spectra from a new dataset with 326 true multi-fault versions from the Defects4J benchmark set containing up to 14 real faults. For all three spectrum types we consistently see substantial reductions of the average wasted efforts at different fault levels, of 30%-90% over the best base metric, and generally similarly large increases in precision and recall, albeit with larger variance across the underlying projects. For the method-level real faults, FLITSR also substantially outperforms GRACE, a state-of-the-art learning-based fault localizer. CCS CONCEPTS• Software and its engineering → Software testing and debugging.

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Section: Threats To Validitymentioning

confidence: 99%

Improving Spectrum-Based Localization of Multiple Faults by Iterative Test Suite Reduction

Callaghan,

Fischer

2023

Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis

Self Cite

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A language-parametric test coverage framework for executable domain-specific languages

Khorram,

Bousse,

Garmendia

et al. 2024

Journal of Systems and Software

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Software Failure Prediction Based On Program State and First-Error Characteristics

Zhu,

Zhang

2024

The Computer Journal

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Evaluating program reliability against software faults involves the work of analyzing program failure against software faults as behavior on failures is fundamental for assessing reliability. Similar failure behavior reflects similar reliability assessment against software faults of same time–space distribution. Since program failure is determined by fault manifestations, working on similarities on fault manifestations helps figuring out failures in similarity as well. In this paper, we propose a novel method to characterize program behavior by defining fine-grained runtime states at assembly-level of code, aiming to capture the very first abnormal manifestation—first-error—after software fault being activated during program run. Failure prediction model and measurement is presented based on similarities reflected by the runtime behaviors obtained. Fault injection experiments are conducted to verify the prediction measurement by utilizing software faults of Orthogonal Defect Classification to inject and MiBench programs to perform. Over 15 000+ times of fault injection by considering type and location of fault were conducted into each program in order to quantify and analyze the sensitivity on first-error and similarity on failure accordingly. The results show that programs having particular structural features (e.g. massive operations with regards to calculations, nested function-calls, case structures and loop structures, etc.) are well characterized toward first-error behaviors by extracting fine-grained states. Similarities on first-error sensitivity can be represented better for these programs as well. Same trend is seen on failure behavior and its prediction measurement.

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Spectrum-based fault localization for context-free grammars

Cited by 10 publications

References 45 publications

Improving Spectrum-Based Localization of Multiple Faults by Iterative Test Suite Reduction

Improving Spectrum-Based Localization of Multiple Faults by Iterative Test Suite Reduction

A language-parametric test coverage framework for executable domain-specific languages

Software Failure Prediction Based On Program State and First-Error Characteristics

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