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An Automated Testing and Debugging Toolkit for Gate-Level Logic Synthesis Applications

Software Engineering · Submitted 17 Jul 2022

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Experimental results show that our fuzzer captures defects in mockturtle, ABC, and LSOracle with 10x smaller testcases and our testcase minimizer extracts minimal failure-inducing cores using 2x fewer oracle calls.

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arXiv ID2207.13487 (version 1)
Pith ID55CGM7XD, Pith's permanent record of this paper and its review
CitationGiovanni De Micheli, Heinz Riener, Siang-Yun Lee. "An Automated Testing and Debugging Toolkit for Gate-Level Logic Synthesis Applications." arXiv:2207.13487 (2022). https://pith.science/paper/55CGM7XD
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,942 input tokens.

What the abstract itself states, as probabilities: claims first 0.07 · states limitation 0.12 · human participants 0.03 · controlled comparison 0.57 · randomized assignment 0.01 · states quantitative result 0.98 · compares with prior results 0.94 · mentions code or data availability 0.07.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Correctness and robustness are essential for logic synthesis applications, but they are often only tested with a limited set of benchmarks. Moreover, when the application fails on a large benchmark, the debugging process may be tedious and time-consuming. In some fields such as compiler construction, automatic testing and debugging tools are well-developed to support developers and provide minimal guarantees on program quality. In this paper, we adapt fuzz testing and delta debugging techniques and specialize them for gate-level netlists commonly used in logic synthesis. Our toolkit improves over similar tools specialized for the AIGER format by supporting other gate-level netlist formats and by allowing a tight integration to provide 10x speed-up. Experimental results show that our fuzzer captures defects in mockturtle, ABC, and LSOracle with 10x smaller testcases and our testcase minimizer extracts minimal failure-inducing cores using 2x fewer oracle calls.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Correctness and robustness are essential for logic synthesis applications, but they are often only tested with a limited set of benchmarks. background 0.86 0.07 0.02 asserted 0.97
s1 Moreover, when the application fails on a large benchmark, the debugging process may be tedious and time-consuming. background 0.91 0.06 0.02 hedged possibility 1.00
s2 In some fields such as compiler construction, automatic testing and debugging tools are well-developed to support developers and provide minimal guarantees on program quality. background 0.97 0.03 0.02 asserted 0.99
s3 In this paper, we adapt fuzz testing and delta debugging techniques and specialize them for gate-level netlists commonly used in logic synthesis. contribution 0.76 0.92 0.10 asserted 0.99
s4 Our toolkit improves over similar tools specialized for the AIGER format by supporting other gate-level netlist formats and by allowing a tight integration to provide 10x speed-up. contribution 0.98 0.91 0.17 asserted 1.00
s5 Experimental results show that our fuzzer captures defects in mockturtle, ABC, and LSOracle with 10x smaller testcases and our testcase minimizer extracts minimal failure-inducing cores using 2x fewer oracle calls. result 1.00 0.96 0.59 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.98.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Correctness and robustness are essential for logic synthesis applications, but they are often only tested with a limited set of benchmarks. Moreover, when the application fails on a large benchmark, the debugging process may be tedious and time-consuming. In some fields such as compiler construction, automatic testing and debugging tools are well-developed to support developers and provide minimal guarantees on program quality. In this paper, we adapt fuzz testing and delta debugging techniques and specialize them for gate-level netlists commonly used in logic synthesis. Our toolkit improves over similar tools specialized for the AIGER format by supporting other gate-level netlist formats and by allowing a tight integration to provide 10x speed-up. Experimental results show that our fuzzer captures defects in mockturtle, ABC, and LSOracle with 10x smaller testcases and our testcase minimizer extracts minimal failure-inducing cores using 2x fewer oracle calls.

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What this paper does with the arXiv papers it cites

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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Uses the method or tool of 1 paper
  • The testing and debugging toolkit described in this section is implemented in mockturtle[Available: <https://github.com/lsils/mockturtle>] as part of the EPFL open-source logic synthesis libraries [ref] .

    The EPFL Logic Synthesis Libraries · Usage Example · 0.89

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