RV-Android: Efficient Parametric Android Runtime Verification, a Brief Tutorial

Daian, Philip; Falcone, Ylìès; Meredith, Patrick; Serbanuta, Traian-Florin; Shiriashi, Shin'ichi; Iwai, Akihito; Roşu, Grigore

doi:10.1007/978-3-319-23820-3_24

Cited by 20 publications

(11 citation statements)

References 11 publications

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“…Monitoring LTL properties dynamically on an Android device is not a new idea. To the best of our knowledge, there are three Android monitoring tools, RV‐Droid, [50] RV‐Android [51] and ADRENALIN‐RV [52]. All these tools monitor LTL properties at the source code level.…”

Section: Related Workmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

“…[42] and RV‐Droid [50] use LTL progression. As an alternative, RV‐Android [51] first translates an LTL specification into a past time linear temporal logic formula and monitors past actions using that formula. Using LTL progression enables us to use reward shaping [53].…”

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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“…Monitoring LTL properties dynamically from an Android device is not a new idea. To the best of our knowledge, there are three Android monitoring tools, RV-Droid [16], RV-Android [14], and ADRENALIN-RV [42]. All these tools monitor LTL properties at the source code level.…”

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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“…Runtime solutions for avoiding and mitigating the impact of failures have been studied in many different contexts, including Web applications [29], mobile applications [31,32,16,15], operative systems [35], and Cloud environments [14].…”

Section: Related Workmentioning

confidence: 99%

Verifying Policy Enforcers

Riganelli

Micucci

Mariani

et al. 2017

Runtime Verification

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Abstract. Policy enforcers are sophisticated runtime components that can prevent failures by enforcing the correct behavior of the software. While a single enforcer can be easily designed focusing only on the behavior of the application that must be monitored, the effect of multiple enforcers that enforce different policies might be hard to predict. So far, mechanisms to resolve interferences between enforcers have been based on priority mechanisms and heuristics. Although these methods provide a mechanism to take decisions when multiple enforcers try to affect the execution at a same time, they do not guarantee the lack of interference on the global behavior of the system. In this paper we present a verification strategy that can be exploited to discover interferences between sets of enforcers and thus safely identify a-priori the enforcers that can co-exist at run-time. In our evaluation, we experimented our verification method with several policy enforcers for Android and discovered some incompatibilities.

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RV-Android: Efficient Parametric Android Runtime Verification, a Brief Tutorial

Cited by 20 publications

References 11 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

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

Verifying Policy Enforcers

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