Performance Analysis of Spotify® for Android with Model-Based Testing

Espada, Ana Rosario; Gallardo, María del Mar Prados; Salmerón, Alberto; Merino, Pedro

doi:10.1155/2017/2012696

Cited by 12 publications

(14 citation statements)

References 18 publications

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“…In previous work , the authors applied the proposed model‐based methodology to analyse the network traffic produced by the Spotify app for Android devices. The implementation of this approach was not integrated into the TRIANGLE testing framework, and some of its parts, such as automatic model extraction or the optimization of app user flows, were not yet designed and implemented.…”

Section: Discussionmentioning

confidence: 99%

“…Model‐based testing can help to automate the generation of app user flows as JSON scripts. Furthermore, if the model is correctly annotated, only the app user flows that are useful to compute any given KPI are generated (preliminary work ).…”

Section: Triangle Testing Frameworkmentioning

confidence: 99%

“…Some of these contributions are new, while others are significant improvements of preliminary work by the authors , which proposed earlier versions of the ideas on automatic test case generation and the method to analyse energy‐related properties. The current paper extends the initial method to generate test cases , where UML state machines were used as the modelling language for user interactions, with a precise definition of the variant of state machines that was finally implemented in the tool chain.…”

Section: Introductionmentioning

confidence: 99%

“…The semantics of both modelling languages to describe user behaviours and EFPs were separately introduced in the previous papers, whereas this paper provides an updated and unified notation for both, making them easier to use as a reference for implementation. The authors used an earlier prototype to analyse traffic and energy in the well‐known application Spotify for Android . This paper presents the evaluation of the integrated approach using a new case study, likewise using a real app such as the Universal Music Player, and evaluates its behaviour in different network scenarios.…”

Section: Introductionmentioning

confidence: 99%

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A formal approach to automatically analyse extra‐functional properties in mobile applications

Espada

Gallardo

Salmerón

et al. 2019

Software Testing Verif & Rel

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Summary This paper presents an integrated approach for testing mobile applications (apps) against a set of extra‐functional properties to be used by app developers. The approach starts with the (manual or automatic) extraction of the interaction model, that is, a formal model of the potential user interactions with the app. The model is constructed to allow a model checking tool to exhaustively extract the so‐called app user flows, that is, the sequences of user actions, that constitute the test cases. In the final step, the app user flows are executed on the app running on real devices. The resulting execution traces are enriched with different measures and verified against a set of extra‐functional properties of interest. The approach has been adapted to analyse several applications running at the same time with several devices supporting the applications. This paper presents the definition and formalization of both the modelling language for the interaction model and the specification language to represent the extra‐functional properties. It also describes a methodology for automatically extracting the model. Finally, it presents an implementation focused on Android apps, which is integrated in the TRIANGLE testing framework, and the evaluation of the approach. © 2019 The Authors. Software Testing, Verification & Reliability Published by John Wiley & Sons Ltd.

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Section: Discussionmentioning

confidence: 99%

Section: Triangle Testing Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A formal approach to automatically analyse extra‐functional properties in mobile applications

Espada

Gallardo

Salmerón

et al. 2019

Software Testing Verif & Rel

Self Cite

View full text Add to dashboard Cite

show abstract

“…In previous work [2][3][4], we presented an approach for automating the analysis of Android mobile apps, based on model-based testing and runtime verification. The former was used to generate a large set of test cases from an app model provided by the user, and then the latter analysed the executions of each one for certain properties of interest.…”

Section: Introductionmentioning

confidence: 99%

Guided test case generation for mobile apps in the TRIANGLE project: work in progress

Panizo

Salmerón

Gallardo

et al. 2017

Proceedings of the 24th ACM SIGSOFT International SPIN Symposium on Model Checking of Software

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The evolution of mobile networks and the increasing number of scenarios for mobile applications requires new approaches to ensure their quality and performance. The TRIANGLE project aims to develop an integrated testing framework that allows the evaluation of applications and devices in different network scenarios. This paper focuses on the generation of user interactions that will be part of the test cases for applications. We propose a method that combines model-based testing and guided search, based on the Key Performance Indicators to be measured, and we have evaluated our proposal with an example. Our ultimate goal is to integrate the guided generation of user flows into the TRIANGLE testing framework to automatically generate and execute test cases. CCS CONCEPTS• Software and its engineering → Software verification and validation; Software testing and debugging; Empirical software validation; KEYWORDSModel-based testing, test case generation, Spin

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An end‐user‐centric test generation methodology for performance evaluation of mobile networked applications

Al‐tekreeti

Abdrabou

Naik

2019

Software Testing Verif & Rel

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We propose a model-based test generation methodology to evaluate the impact of the interaction of the wireless network and application configurations on the performance of mobile networked applications. We consider waiting time delay to model wireless network quality. We classify mobile applications into two groups. Group I represents applications where end-user experience is mainly affected by waiting time delay during service consumption, while group II represents applications where end-user experience is affected by waiting time delay before service consumption. Test generation is formulated as an inversion problem. However, for group I applications, solving the inversion problem is expensive. Therefore, we utilize metamorphic testing to mitigate the cost of test oracles. We formulate metamorphic test generation as maximization of the distance between seed and follow-up test cases. Two test coverage criteria are proposed: user experience and user-experience-and-input interaction. Network models are developed for a mobile device that has network access through a WiFi hot spot and uses either transmission control protocol or user datagram protocol. Two mobile applications are used to demonstrate the methodology: multimedia streaming and web browsing. Application of the methodology when user actions are taken into consideration is also addressed. The effectiveness of the methodology is evaluated using two metrics: the incurred time cost and redundancy in the generated test suite. The obtained results show the advantage of casting test generation as an inversion problem, compared with random testing. For apps with intensive performance models, combining metamorphic testing with the methodology has tremendously reduced the cost of test oracles. 15 of 38 state y. Therefore, the SUT desired behaviour introduces the following two constraints (Procedure 3.S1), where B, M , and L are defined as an integer number of multimedia frames:M Ä B and L Ä M 1:(4) 4.1.2. The wireless network model. The end user is streaming via a WiFi hot spot and using the UDP. We assume all the fluctuations in the wireless link and the queuing effects of the different routers along the path from the server to the client device manifest as a time delay. That is, packet loss is negligible. A reusable network model for the UDP scenario was developed in our previous work [15]. We have developed two analytical expressions for the mean and variance of packet interarrival time delay. The probability distribution was matched with the hyper-Erlang distribution. The network impact is captured by three operating parameters (NOPs): the data rate (D), the mean rate of frame arrivals into the access point ( ) per user, and the number of end users (N ) connected to the AP. The data rate according to the IEEE 802.11 a/g standard can be in one of the following values: 6, 9, 12, 18, 24, 32, 48, or 54 Mbps. It mainly relates to the quality of the wireless connection between the AP and the end user. Regarding the number of users N , the network model is validated...

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Performance Analysis of Spotify® for Android with Model-Based Testing

Cited by 12 publications

References 18 publications

A formal approach to automatically analyse extra‐functional properties in mobile applications

A formal approach to automatically analyse extra‐functional properties in mobile applications

Guided test case generation for mobile apps in the TRIANGLE project: work in progress

An end‐user‐centric test generation methodology for performance evaluation of mobile networked applications

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