Checking Liveness Properties of Concurrent Systems by Reinforcement Learning

Araragi, Tadashi; Cho, Seung Mo

doi:10.1007/978-3-540-74128-2_6

Cited by 8 publications

(5 citation statements)

References 4 publications

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“…Araragi and Cho [70] targeted the production of counterexamples using reinforcement learning. The authors kept track of the premise occurrence at the state space level, and rewarded explorations that stayed on paths with possible contradiction that can generate counterexamples.…”

Section: B Ml-based Model Checking For Vulnerability Analysismentioning

confidence: 99%

A Survey on Hardware Vulnerability Analysis Using Machine Learning

Pan

Mishra

2022

IEEE Access

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Electronic systems rely on efficient hardware, popularly known as system-on-chip (SoC), to support its core functionalities. A typical SoC consists of diverse components gathered from third-party vendors to reduce SoC design cost and meet time-to-market constraints. Unfortunately, the participation of third-party companies in global supply chain introduces potential security vulnerabilities. There is a critical need to efficiently detect and mitigate hardware vulnerabilities. Machine learning has been successfully used in hardware security verification as well as development of effective countermeasures. There are recent surveys on hardware Trojan detection using machine learning. To the best of our knowledge, there are no comprehensive surveys on utilization of machine learning techniques for detection and mitigation of a wide variety of hardware vulnerabilities including malicious implants (e.g., hardware Trojans), sidechannel leakage, reverse engineering, and supply-chain vulnerabilities (e.g., counterfeiting, overbuilding and recycling). In this paper, we provide a comprehensive survey of hardware vulnerability analysis using machine learning techniques. Specifically, we discuss how existing approaches effectively utilize machine learning algorithms for hardware security verification using simulation-based validation, formal verification as well as side-channel analysis.

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Section: B Ml-based Model Checking For Vulnerability Analysismentioning

confidence: 99%

A Survey on Hardware Vulnerability Analysis Using Machine Learning

Pan

Mishra

2022

IEEE Access

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“…Two studies [44,45] use RL for model checking LTL properties. These studies do not prove LTL properties but focus on finding counterexamples.…”

Section: Related Workmentioning

confidence: 99%

Functional test generation from UI test scenarios using reinforcement learning for android applications

Koroglu

Şen

2020

Software Testing Verif & Rel

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Summary With the ever‐growing Android graphical user interface (GUI) application market, there have been many studies on automated test generation for Android GUI applications. These studies successfully demonstrate how to detect fatal exceptions and achieve high coverage with fully automated test generation engines. However, it is unclear how many GUI functions these engines manage to test. The current best practice for the functional testing of Android GUI applications is to design user interface (UI) test scenarios with a non‐technical and human‐readable language such as Gherkin and implement Java/Kotlin methods for every statement of all the UI test scenarios. Writing tests for UI test scenarios is hard, especially when some scenario statements are high‐level and declarative, so it is not clear what actions should the generated test perform. We propose the Fully Automated Reinforcement LEArning‐Driven specification‐based test generator for Android (FARLEAD‐Android). FARLEAD‐Android first translates the UI test scenario to a GUI‐level formal specification as a linear‐time temporal logic (LTL) formula. The LTL formula guides the test generation and acts as a specified test oracle. By dynamically executing the application under test (AUT), and monitoring the LTL formula, FARLEAD‐Android learns how to produce a witness for the UI test scenario, using reinforcement learning (RL). Our evaluation shows that FARLEAD‐Android is more effective and achieves higher performance in generating tests for UI test scenarios than three known engines: Random, Monkey and QBEa. To the best of our knowledge, FARLEAD‐Android is the first fully automated mobile GUI testing engine that uses formal specifications.

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“…Two studies [4,8] use RL for model checking LTL properties. These studies do not prove LTL properties but focus on finding counterexamples.…”

Section: Related Workmentioning

confidence: 99%

Reinforcement Learning-Driven Test Generation for Android GUI Applications using Formal Specifications

Koroglu,

Sen

2019

Preprint

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There have been many studies on automated test generation for mobile Graphical User Interface (GUI) applications. These studies successfully demonstrate how to detect fatal exceptions and achieve high code and activity coverage with fully automated test generation engines. However, it is unclear how many GUI functions these engines manage to test. Furthermore, these engines implement only implicit test oracles. We propose Fully Automated Reinforcement LEArning-Driven Specification-Based Test Generator for Android (FARLEAD-Android). FARLEAD-Android accepts a GUI-level formal specification as a Linear-time Temporal Logic (LTL) formula. By dynamically executing the Application Under Test (AUT), it learns how to generate a test that satisfies the LTL formula using Reinforcement Learning (RL). The LTL formula does not just guide the test generation but also acts as a specified test oracle, enabling the developer to define automated test oracles for a wide variety of GUI functions by changing the formula. Our evaluation shows that FARLEAD-Android is more effective and achieves higher performance in generating tests for specified GUI functions than three known approaches, Random, Monkey, and QBEa. To the best of our knowledge, FARLEAD-Android is the first fully automated mobile GUI testing engine that uses formal specifications. CCS CONCEPTS• Theory of computation → Modal and temporal logics; • Software and its engineering → Software testing and debugging; Functionality; • Computing methodologies → Reinforcement learning.

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Checking Liveness Properties of Concurrent Systems by Reinforcement Learning

Cited by 8 publications

References 4 publications

A Survey on Hardware Vulnerability Analysis Using Machine Learning

A Survey on Hardware Vulnerability Analysis Using Machine Learning

Functional test generation from UI test scenarios using reinforcement learning for android applications

Reinforcement Learning-Driven Test Generation for Android GUI Applications using Formal Specifications

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