Bounded Rational Search for On-the-Fly Model Checking of LTL Properties

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 [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

Sen

2020

Softw. Test. Verif. Reliab.

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“…However, as different from these studies, our approach focuses on using reinforcement learning to replace exhaustive model checking for mission-plan synthesis of multi-agent systems, so that the statespace explosion is alleviated. To the best of our knowledge, the first attempt to solve the state-space-explosion problem of model checking using reinforcement learning is done by Behjati et al [3]. These authors propose a bounded rational verification approach for on-the-fly model checking.…”

Section: Related Workmentioning

confidence: 99%

Verifiable and Scalable Mission-Plan Synthesis for Autonomous Agents

Enoiu

Seceleanu

et al. 2020

Formal Methods for Industrial Critical Systems

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The problem of synthesizing mission plans for multiple autonomous agents, including path planning and task scheduling, is often complex. Employing model checking alone to solve the problem might not be feasible, especially when the number of agents grows or requirements include real-time constraints. In this paper, we propose a novel approach called MCRL that integrates model checking and reinforcement learning to overcome this limitation. Our approach employs timed automata and timed computation tree logic to describe the autonomous agents' behavior and requirements, and trains the model by a reinforcement learning algorithm, namely Q-learning, to populate a table used to restrict the state space of the model. Our method provides means to synthesize mission plans for multi-agent systems whose complexity exceeds the scalability boundaries of exhaustive model checking, but also to analyze and verify synthesized mission plans to ensure given requirements. We evaluate the proposed method on various scenarios involving autonomous agents, as well as present comparisons with two similar approaches, TAMAA and UPPAAL STRATEGO. The evaluation shows that MCRL performs better for a number of agents greater than three.

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“…• Simulator backend for Rebeca and Timed Rebeca in Erlang [78] • Analysis backend for Timed Rebeca in Real-Time Maude [80] • Model checking tool chain for Probabilistic Timed Rebeca [59] using PRISM and IMCA • SysFier: model checking tool for SystemC designs [77] • Sarir: µCRL2 model checker for Rebeca [58] • Distributed model checking tool for Rebeca models [66] • Model checking tool for Broadcasting Rebeca [94] • Bounded Rational LTL model checker of Rebeca [12] • Guided search engine for deadlock detection [84] 2.3.2 ABS.…”

Section: :10mentioning

confidence: 99%

A Survey of Active Object Languages

et al. 2017

Self Cite

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International audienceTo program parallel systems efficiently and easily, a wide range of programming models have been proposed, each with different choices concerning synchronization and communication between parallel entities. Among them, the actor model is based on loosely coupled parallel entities that communicate by means of asynchronous messages and mailboxes. Some actor languages provide a strong integration with object-oriented concepts; these are often called active object languages. This paper reviews four major actor and active object languages and compares them according to carefully chosen dimensions that cover central aspects of the programming paradigms and their implementation

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Bounded Rational Search for On-the-Fly Model Checking of LTL Properties

Cited by 16 publications

References 19 publications

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

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

Verifiable and Scalable Mission-Plan Synthesis for Autonomous Agents

A Survey of Active Object Languages

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