On the reliability of coverage-based fuzzer benchmarking

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Evaluating Fuzz Testing

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A Hitchhiker's guide to statistical tests for assessing randomized algorithms in software engineering

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FairFuzz: a targeted mutation strategy for increasing greybox fuzz testing coverage

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Coverage is not strongly correlated with test suite effectiveness

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Coverage-Based Greybox Fuzzing as Markov Chain

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Search-Based Software Testing: Past, Present and Future

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Smart Greybox Fuzzing

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FuzzBench: an open fuzzer benchmarking platform and service

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Fuzzing: Challenges and Reflections

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An Empirical Study on Mutation, Statement and Branch Coverage Fault Revelation That Avoids the Unreliable Clean Program Assumption

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Where is the bug and how is it fixed? an experiment with practitioners

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Boosting fuzzer efficiency: an information theoretic perspective

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Assertions are strongly correlated with test suite effectiveness

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Seed selection for successful fuzzing

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Are mutation scores correlated with real fault detection?

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CoREBench: studying complexity of regression errors

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Sound empirical evidence in software testing

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BugsJS: a Benchmark of JavaScript Bugs

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Code coverage at Google

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BugSwarm: Mining and Continuously Growing a Dataset of Reproducible Failures and Fixes

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Grey-Box Concolic Testing on Binary Code

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Code coverage and test suite effectiveness: Empirical study with real bugs in large systems

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Semantic bug seeding: a learning-based approach for creating realistic bugs

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Guidelines for Coverage-Based Comparisons of Non-Adequate Test Suites

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Bug synthesis: challenging bug-finding tools with deep faults

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Revisiting the relationship between fault detection, test adequacy criteria, and test set size

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Controlling factors in evaluating path-sensitive error detection techniques

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EvilCoder

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Community expectations for research artifacts and evaluation processes

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Is Branch Coverage a Good Measure of Testing Effectiveness?

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Generating Effective Test Suites by Combining Coverage Criteria

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Evaluating Synthetic Bugs

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FuzzTastic

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A Practical, Principled Measure of Fuzzer Appeal: A Preliminary Study

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CodaMosa: Escaping Coverage Plateaus in Test Generation with Pre-trained Large Language Models

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Large Language Model guided Protocol Fuzzing

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Fuzz4ALL: Universal Fuzzing with Large Language Models

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Effective test generation using pre-trained Large Language Models and mutation testing

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Effectively Generating Vulnerable Transaction Sequences in Smart Contracts with Reinforcement Learning-guided Fuzzing

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SoK: Prudent Evaluation Practices for Fuzzing

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Vulseye: Detect Smart Contract Vulnerabilities via Stateful Directed Graybox Fuzzing

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Test smells 20 years later: detectability, validity, and reliability

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Open Problems in Fuzzing RESTful APIs: A Comparison of Tools

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Sequence-Oriented DBMS Fuzzing

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Guiding Greybox Fuzzing with Mutation Testing

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Reachable Coverage: Estimating Saturation in Fuzzing

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ShapFuzz: Efficient Fuzzing via Shapley-Guided Byte Selection

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Icicle: A Re-designed Emulator for Grey-Box Firmware Fuzzing

Chesser, M., Nepal‬, ‪., & Ranasinghe, D. C. (2023) · ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) · doi:10.1145/3597926.3598039 · Cited by 16 · see citing works

AFLNet Five Years Later: On Coverage-Guided Protocol Fuzzing

Meng, R., Pham, V.-T., Böhme, M., & Roychoudhury, A. (2025) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2025.3535925 · Cited by 14 · see citing works

DatAFLow : Toward a Data-Flow-Guided Fuzzer

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Rustlantis: Randomized Differential Testing of the Rust Compiler

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SBFT Tool Competition 2023 - Fuzzing Track

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Green Fuzzing: A Saturation-Based Stopping Criterion using Vulnerability Prediction

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Program Environment Fuzzing

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A Usability Evaluation of AFL and libFuzzer with CS Students

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Planning to Guide LLM for Code Coverage Prediction

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FOX: Coverage-guided Fuzzing as Online Stochastic Control

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No Peer, no Cry: Network Application Fuzzing via Fault Injection

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Security Evaluation of Smart Contracts based on Code and Transaction - A Survey

Su, J., Liu, J., Nan, Y., & Li, Y. (2022) · International Conference on Service Science (ICSS) · doi:10.1109/icss55994.2022.00016 · Cited by 7 · see citing works

DiPri : Distance-Based Seed Prioritization for Greybox Fuzzing

Qian, R., Zhang, Q., Fang, C., Yang, D., Li, S., Li, B., & Chen, Z. (2024) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3654440 · Cited by 7 · see citing works

Predictive Context-sensitive Fuzzing

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SoK: Where to Fuzz? Assessing Target Selection Methods in Directed Fuzzing

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SLOPT: Bandit Optimization Framework for Mutation-Based Fuzzing

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Green Fuzzer Benchmarking

Ounjai, J., Wüstholz, V., & Christakis, M. (2023) · ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) · doi:10.1145/3597926.3598144 · Cited by 6 · see citing works

Automated Test Generation for Medical Rules Web Services: A Case Study at the Cancer Registry of Norway

Laaber, C., Yue, T., Ali, S., Schwitalla, T., & Nygård, J. F. (2023) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3611643.3613882 · Cited by 6 · see citing works

Revisiting Neural Program Smoothing for Fuzzing

Nicolae, M.-I., Eisele, M., & Zeller, A. (2023) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3611643.3616308 · Cited by 6 · see citing works

An Empirical Examination of Fuzzer Mutator Performance

Kukucka, J., Pina, L., Ammann, P., & Bell, J. (2024) · ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) · doi:10.1145/3650212.3680387 · Cited by 5 · see citing works

No-Fuzz: Efficient Anti-fuzzing Techniques

Zhou, Z., Wang, C., & Zhao, Q. (2023) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-25538-0_38 · Cited by 5 · see citing works

DiPri: Distance-Based Seed Prioritization for Greybox Fuzzing (Registered Report)

Qian, R., Zhang, Q., Fang, C., & Chen, Z. (2023) · International Fuzzing Workshop (FUZZING) · doi:10.1145/3605157.3605172 · Cited by 4 · see citing works

Synergistic Verification of Hardware Peripherals through Virtual Prototype Aided Cross-Level Methodology Leveraging Coverage-Guided Fuzzing and Co-Simulation

Ahmadi-Pour, S., Logemann, M., Herdt, V., & Drechsler, R. (2023) · Chips · doi:10.3390/chips2030012 · Cited by 4 · see citing works

Forward-porting and its limitations in fuzzer evaluation

Elahi, H., & Wang, G. (2024) · Information Sciences · doi:10.1016/j.ins.2024.120142 · Cited by 4 · see citing works

DeepDiffer: Find Deep Learning Compiler Bugs via Priority-guided Differential Fuzzing

Lin, K., Song, X., Zeng, Y., & Guo, S. (2023) · IEEE International Conference on Software Quality, Reliability and Security (QRS) · doi:10.1109/qrs60937.2023.00066 · Cited by 5 · see citing works

Make out like a (Multi-Armed) Bandit: Improving the Odds of Fuzzer Seed Scheduling with T-Scheduler

Luo, S., Herrera, A., Quirk, P., Chase, M. K., Ranasinghe, D. C., & Kanhere, S. S. (2024) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/3634737.3637639 · Cited by 4 · see citing works

Proceedings of the 2024 IEEE/ACM First International Conference on AI Foundation Models and Software Engineering

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How We Built Cedar: A Verification-Guided Approach

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Model-Guided Fuzzing of Distributed Systems

Gulcan, B., Ozkan, B. K., Majumdar, R., & Nagendra, S. (2025) · Proceedings of the ACM on Programming Languages · doi:10.1145/3763060 · Cited by 3 · see citing works

Metamong: Detecting Render-Update Bugs in Web Browsers through Fuzzing

Song, S., & Lee, B. (2023) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3611643.3616336 · Cited by 3 · see citing works

FunFuzz: Greybox Fuzzing with Function Significance

Qian, R., Zhang, Q., Fang, C., Guo, L., & Chen, Z. (2024) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3702974 · Cited by 3 · see citing works

FuzzTheREST: An Intelligent Automated Black-Box RESTful API Fuzzer

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The Havoc Paradox in Generator-Based Fuzzing

Li, A., Huang, M., Vikram, V., Lemieux, C., & Padhye, R. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3742894 · Cited by 3 · see citing works

ZTaint-Havoc: From Havoc Mode to Zero-Execution Fuzzing-Driven Taint Inference

Xie, Y., Zhang, W., & She, D. (2025) · Proceedings of the ACM on software engineering. · doi:10.1145/3728916 · Cited by 3 · see citing works

Practical Anti-Fuzzing Techniques With Performance Optimization

Zhou, Z., & Wang, C. (2023) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2023.3301883 · Cited by 3 · see citing works

BazzAFL: Moving Fuzzing Campaigns Towards Bugs via Grouping Bug-Oriented Seeds

Ye, K., Zhu, X., Xiao, X., Wen, S., Xue, M., & Xiang, Y. (2024) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2024.3391795 · Cited by 3 · see citing works

DiPri : Distance-Based Seed Prioritization for Greybox Fuzzing—RCR Report

Qian, R., Zhang, Q., Fang, C., Yang, D., Li, S., Li, B., & Chen, Z. (2024) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3701298 · Cited by 3 · see citing works

AutoFuzz: automatic fuzzer-sanitizer scheduling with multi-armed bandit

Gao, Y., Zeng, W., Liu, S., & Zeng, Y. (2025) · Software Quality Journal · doi:10.1007/s11219-025-09707-6 · Cited by 3 · see citing works

Program Feature-Based Benchmarking for Fuzz Testing

Miao, M., Kummita, S., Bodden, E., & Wei, S. (2025) · Proceedings of the ACM on software engineering. · doi:10.1145/3728899 · Cited by 2 · see citing works

On Evaluating Fuzzers with Context-Sensitive Fuzzed Inputs: A Case Study on PKCS#1-v1.5

Hasan, S. M., Козырева, П., & Hoque, E. (2025) · IEEE/ACM International Workshop on Search-Based Software Testing (SBST) · doi:10.1109/sbft66712.2025.00013 · Cited by 2 · see citing works

In Bugs We Trust? On Measuring the Randomness of a Fuzzer Benchmarking Outcome

Madadi, A., Lee, S., Aschermann, C., & Böhme, M. (2026) · Proceedings of the ACM on software engineering. · doi:10.1145/3797112 · Cited by 2 · see citing works

RCABench: Open Benchmarking Platform for Root Cause Analysis

Nishimura, K., Sugiyama, Y., Koike, Y., Motoda, M., Kitagawa, T., Takatera, T., & Kurogome, Y. (2023) · Workshop on Binary Analysis Research (BAR) · doi:10.14722/bar.2023.23004 · Cited by 2 · see citing works

Triereme: Speeding up hybrid fuzzing through efficient query scheduling

Geretto, E., Hohnerlein, J., Giuffrida, C., Bos, H., van der Kouwe, E., & Gleissenthall, K. V. (2023) · Annual Computer Security Applications Conference · doi:10.1145/3627106.3627173 · Cited by 2 · see citing works

TR-Fuzz: A syntax valid tool for fuzzing C compilers

Zhang, C., Chen, J., Cai, S., Wen, Z., Sosu, R. N. A., & Chen, H. (2024) · Science of Computer Programming · doi:10.1016/j.scico.2024.103155 · Cited by 2 · see citing works

On the Analysis of Coverage Feedback in a Fuzzing Proprietary System

Jang, D., Kim, J., Kim, J., Im, W., Jeong, M., Choi, B.-C., & Kil, C. (2024) · Applied Sciences · doi:10.3390/app14135939 · Cited by 2 · see citing works

Evaluating API-Level Deep Learning Fuzzers: A Comprehensive Benchmarking Study

Harzevili, N. S., Wei, M., Mohajer, M. M., Pham, H. V., & Wang, S. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3729533 · Cited by 3 · see citing works

An Empirical Study on AST-level mutation-based fuzzing techniques for JavaScript Engines

Tang, S., Liu, S., Wang, J., & Zhang, X. (2023) · Asia-Pacific Symposium on Internetware · doi:10.1145/3609437.3609440 · Cited by 1 · see citing works

CatchFuzz: Reliable active anti-fuzzing techniques against coverage-guided fuzzer

Kim, H. Y., & Lee, D. H. (2024) · Computers & Security · doi:10.1016/j.cose.2024.103904 · Cited by 1 · see citing works

Fuzzing: On Benchmarking Outcome as a Function of Benchmark Properties

Wolff, D., Böhme, M., & Roychoudhury, A. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3732936 · Cited by 1 · see citing works

SoK: From Systematization to Best Practices in Fuzz Driver Generation

Yan, Q., Huang, M., Cao, H., & Lu, S. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-9101-2_18 · Cited by 1 · see citing works

fFuzz: A State-Aware Function-Level Fuzzing Framework for Smart Contract Vulnerabilities Detection

Li, C., Lu, B., Zhang, W., Yang, K., & Zhu, H. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-3543-9_2 · Cited by 1 · see citing works

Do Unit Proofs Work? An Empirical Study of Compositional Bounded Model Checking for Memory Safety Verification

Amusuo, P. C., Cochell, O., Lievre, T. L., Patil, P. V., Machiry, A., & Davis, J. C. (2026) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3744916.3764525 · Cited by 1 · see citing works

RandSet: Randomized Corpus Reduction for Fuzzing Seed Scheduling

Xie, Y., Zhang, K. F., Liu, Y., Yang, R., Chen, P., Wang, S., & She, D. (2026) · Proceedings of the ACM on Programming Languages · doi:10.1145/3798257 · Cited by 1 · see citing works

Fuzzing and Symbolic Execution for Multipath Malware Tracing: Bridging Theory and Practice via Survey and Experiments

Botacin, M. F. (2024) · Digital Threats Research and Practice · doi:10.1145/3700147 · Cited by 1 · see citing works

Effective Unit Test Generation for Java Null Pointer Exceptions

Lee, M.-H., Bak, J., Moon, S., Jhi, Y., & Oh, H. (2024) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/3691620.3695484 · Cited by 1 · see citing works

On Understanding and Forecasting Fuzzers Performance with Static Analysis

Zhang, D., Fioraldi, A., & Balzarotti, D. (2024) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3658644.3670348 · Cited by 1 · see citing works

Efficient Fuzzing Infrastructure for Pointer-to-Object Association

Ling, H., Huang, H., Cai, Y., & Zhang, C. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3730580 · Cited by 1 · see citing works

Tumbling Down the Rabbit Hole: How do Assisting Exploration Strategies Facilitate Grey-Box Fuzzing?

Wu, M., Xiang, J., Chen, K., Di, P., Tan, S. H., Cui, H., & Zhang, Y. (2025) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse55347.2025.00044 · Cited by 1 · see citing works

Effective fuzz testing via model-guided program synthesis

Zhang, C. (2023) · doi:10.32657/10356/169112

Structures of the data and the information reuse based in-memory fuzzing

Álvarez-Pérez, D., Fernández‐Veiga, M., & Vilas, A. F. (2023) · doi:10.23919/jnic58574.2023.10205765

Towards rigorous evaluation of binary testing and analysis

Bundt, J. (2023) · doi:10.17760/d20560780

BugOss: A benchmark of real-world regression bugs for empirical investigation of regression fuzzing techniques

Kim, J., & Hong, S. (2024) · Journal of Systems and Software · doi:10.1016/j.jss.2024.112119

Design and Implementation of a Coverage-Guided Ruby Fuzzer

Schwager, M., Klemba, D., & Dykstra, J. (2024) · Cyber Security Experimentation and Test Workshop (CSET) · doi:10.1145/3675741.3675749

Inconsistencies in TeX-Produced Documents

Tan, J., & Rigger, M. (2024) · ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) · doi:10.1145/3650212.3680370

LOOL: Low-Overhead, Optimization-Log-Guided Compiler Fuzzing (Registered Report)

Schwarcz, F., Berlakovich, F., Barany, G., & Mössenböck, H. (2024) · doi:10.1145/3678722.3685533

Poster: YFuzz: Data-Driven Fuzzing

Chang, Y., Huang, C.-C., Mori, T., & Hsiao, H. (2024) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3658644.3691420

A Metric for Measuring the Impact of Rare Paths on Program Coverage

Amour, L. S., Tilevich, E., & Gulzar, M. A. (2025) · IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) · doi:10.1109/saner64311.2025.00042

HSPFuzzer: High-Speed Network Protocol Fuzzing With Connection Reuse

Xia, Z., Zeng, Y., Song, X., Guo, S., & Wu, T. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3590178

AceCov: Auxiliary Composite Edge Coverage for Fuzzing

Yoshida, H., Sugiyama, Y., & Shioya, R. (2025) · IEEE European Symposium on Security and Privacy · doi:10.1109/eurosp63326.2025.00062

Understanding the Potentially Confounding Effect of Test Suite Size in Test Effectiveness Evaluation

Lu, Z., Wang, Y., Rong, Y., Huang, Y., Wang, X., Zhang, P., Gao, S., Sun, M., Yang, Y., Li, Y., et al. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3748504

SBFT’25 Competition Report — Fuzzing Track

Crump, A., Sihag, S., & Leonelli, M. (2025) · IEEE/ACM International Workshop on Search-Based Software Testing (SBST) · doi:10.1109/sbft66712.2025.00008

From Coverage to Causes: Data-Centric Fuzzing for JavaScript EnginesPreprint

Ganguly, K. K., & Menzies, T. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5686732

From Coverage to Causes: Data-Centric Fuzzing for JavaScript EnginesPreprint

Ganguly, K. K., & Menzies, T. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5686730

Function Clustering-Based Fuzzing Termination: Toward Smarter Early Stopping

Ding, L., Yang, W., & Xue, Y. (2025) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase63991.2025.00145

Towards Path-Aware Coverage-Guided Fuzzing

Priamo, G., D’Elia, D. C., Payer, M., & Querzoni, L. (2026) · Proceedings of the CGO/Proceedings of the ... CGO · doi:10.1109/cgo68049.2026.11395191

Sagitta: Facilitating Post-Fuzzing Root Cause Analysis via Data Flow Differencing

Aoki, K., & Shinagawa, T. (2025) · Annual Computer Security Applications Conference · doi:10.1109/acsac67867.2025.00017

Optimized mutation scheduling for fuzzing based on estimation of distribution algorithm

Lv, G., Chen, J., Chen, H., Cai, S., & Zhang, W. (2026) · Cybersecurity · doi:10.1186/s42400-025-00461-7

SeedAIchemy: LLM-Driven Seed Corpus Generation for Fuzzing

Wen, A., Alzahrani, N. A., Jiang, J., Joe, A., Shieh, K., Zhang, A. K., Alomair, B., & Wagner, D. (2025) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw69685.2025.00149

LOOL: Low-Overhead, Optimization-Log-Guided Compiler Fuzzing

Berlakovich, F., Schwarcz, F., Barany, G., Mössenböck, H., & Brunthaler, S. (2026) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3807508

PROTOCOL-ORIENTED FUZZ TESTING OF TELEMETRY AND COMMAND MODULES OF UAV CONTROL SYSTEMS IMPLEMENTED IN RUST

Nyzhnyk, A., & Kotliarov, O. Y. (2026) · Komp'ûternì sistemi ta merežì · doi:10.23939/csn2026.01.193

From coverage to causes: Data-centric fuzzing for Javascript engines

Ganguly, K. K., & Menzies, T. (2026) · Information and Software Technology · doi:10.1016/j.infsof.2026.108211

SemuFuzz: An efficient fuzzing approach for deep learning libraries based on semantic representation

Chen, Z., Chen, J., Cai, S., Chen, J., & Gu, W. (2026) · Journal of Systems and Software · doi:10.1016/j.jss.2026.112999

pPatch: Automated Vulnerability Unpatching

Jing, T., Ding, P., Xu, M., Hu, Y., Yu, Z., & Mu, D. (2026) · Proceedings of the ACM on software engineering. · doi:10.1145/3808201

Reality Check: Independent Evaluation of Modern Grey-Box Fuzzing Techniques

Frolikov, P. (2026) · IEEE Secure Development Conference (SecDev) · doi:10.1145/3805773.3806004

Keynote Talk: Adversarial Benchmarking An Urgent Call for a Stronger Signal of Technological Progress

Böhme, M. (2026) · IEEE International Conference on Software Testing, Verification and Validation (ICST) · doi:10.1109/icst69053.2026.00097

SoK: A Systematic Review of Integration and Reproducibility of Fuzzing Research into AFL++

Schiller, N., Bars, N., Schloegel, M., & Holz, T. (2026) · IEEE European Symposium on Security and Privacy · doi:10.1109/eurosp68448.2026.00074

FrameShift: Resizing Fuzzer Inputs Without Breaking Them

Green, H., Goues, C. L., & Brown, F. (2026) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3744916.3787765

In Bugs We Trust? On Measuring the Randomness of a Fuzzer Benchmarking Outcome

Madadi, A., Lee, S., Aschermann, C., & Böhme, M. (2026) · Proceedings of the ACM on software engineering. · doi:10.1145/3797112 · Cited by 2 · see citing works

FuzzTastic: A Fine-grained, Fuzzer-agnostic Coverage Analyzer

Lipp, S., Elsner, D., Hutzelmann, T., Bănescu, S., Pretschner, A., & Böhme, M. (2022) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse-companion55297.2022.9793832 · Cited by 1 · see citing works

Refining Code Coverage to Better Reflect Fuzzing Performance

Lipp, S., Pretschner, A., & München, T. U. (2025)

Comparing Fuzzers on a Level Playing Field with FuzzBench

Asprone, D., Metzman, J., Arya, A., Guizzo, G., & Sarro, F. (2022) · IEEE International Conference on Software Testing, Verification and Validation (ICST) · doi:10.1109/icst53961.2022.00039 · Cited by 19 · see citing works

Fuzzing: On Benchmarking Outcome as a Function of Benchmark Properties

Wolff, D., Böhme, M., & Roychoudhury, A. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3732936 · Cited by 1 · see citing works