Black-Box Bug Amplification for Multithreaded Software

Yeshayahu Weiss (), Gal Amram, Achiya Elyasaf, Eitan Farchi, Oded Margalit and Gera Weiss
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Yeshayahu Weiss: Department of Computer Science, Ben-Gurion University of the Negev, Be’er Sheva 8410501, Israel
Gal Amram: IBM Research, Haifa 3498825, Israel
Achiya Elyasaf: Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Be’er Sheva 8410501, Israel
Eitan Farchi: IBM Research, Haifa 3498825, Israel
Oded Margalit: Department of Computer Science, Ben-Gurion University of the Negev, Be’er Sheva 8410501, Israel
Gera Weiss: Department of Computer Science, Ben-Gurion University of the Negev, Be’er Sheva 8410501, Israel

Mathematics, 2025, vol. 13, issue 18, 1-42

Abstract: Bugs, especially those in concurrent systems, are often hard to reproduce because they manifest only under rare conditions. Testers frequently encounter failures that occur only under specific inputs, often at low probability. We propose an approach to systematically amplify the occurrence of such elusive bugs. We treat the system under test as a black-box system and use repeated trial executions to train a predictive model that estimates the probability of a given input configuration triggering a bug. We evaluate this approach on a dataset of 17 representative concurrency bugs spanning diverse categories. Several model-based search techniques are compared against a brute-force random sampling baseline. Our results show that an ensemble stacking classifier can significantly increase bug occurrence rates across nearly all scenarios, often achieving an order-of-magnitude improvement over random sampling. The contributions of this work include the following: (i) a novel formulation of bug amplification as a rare-event classification problem; (ii) an empirical evaluation of multiple techniques for amplifying bug occurrence, demonstrating the effectiveness of model-guided search; and (iii) a practical, non-invasive testing framework that helps practitioners to expose hidden concurrency faults without altering the internal system architecture.

Keywords: concurrency bugs; bug reproduction; rare-event detection; model-based testing; search-based software testing; black-box testing; ensemble methods; noise-tolerant learning; probabilistic bug amplification (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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