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MAuto: Automatic Mobile Game Testing Tool Using Image-Matching Based Approach

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Postby Gardazil В» 25.03.2019

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The exponential increase in the speed of the mobile industry has led to a decreasing quality in many associated mobile apps. Besides, the number of distinct Android devices reached thousands. This challenged the development of universally accepted test applications that can run on all devices. This paper focuses on the development of a new mobile game testing framework, referred to, MAuto.

MAuto records the user actions in the game and replays the tests on any Android device. MAuto uses image recognition, through AKAZE features, to record the test cases and the Appium framework to replay the user actions automatically.

The feasibility of the developed tool has been demonstrated through testing on the Clash of Clans mobile game. In the era of mobile computing and internet technology, mobile gaming has seen a huge increase, gaining almost all population groups. According to SmartInsights Chaffey , there are about 1. Besides, with the development of smartphones, fast mobile broadband and platform availability, mobile gaming has moved deeper into the broader culture of individuals and communities.

On the other hand, the ever-growing popularity of Android that constantly attract new developers and business, has unfortunately led to a wide discrepancy of distinct Android devices employed. As such, ensuring efficient and reliable testing of newly developed mobile game applications becomes of paramount importance and one of the toughest challenges faced by game developers, service providers as well as regulators.

This is often referred to as quality assurance QA testing, which focuses on identifying technical problems with the games. Although QA techniques appear almost at every stage of the software development lifecycle, starting from requirement eliciting to product deployment and maintenance where a special interest is attributed to testing automation.

The latter is an integral part of a continuous integration pipeline Novak where simple automated tests are used for basic program elements, such as individual class methods or separate functions. Especially, test automation reduces the time, cost and resources, while enhancing the reliability through exposure to a large number of test cases that cannot be performed solely by human interaction in practice.

Compared to traditional desktop-applications, test automation for mobile applications bears additional challenges. First, through sandboxing, only limited access to internal processes is provided, which challenges the developers to optimize resource allocation.

Second, the general user interface navigation of mobile apps is vulnerable and hard to control due to uncertainty pervading the response time of the interface s.

This includes, for instance, grab and hold like interactions. This problem is also referred to as the fragile test issue pointed out by Meszaros Third, mobile devices are often in a steady movement, which can cause the currently executed test-case for an app to break down.

Fourth, often the complexity of the allocation task together with resource limitations cause a change in size and resolution of the screen, which, in turn, makes any user-interface based test likely to fail.

Fifth, despite the effort to harmonize the software-hardware configuration in mobile platforms, the number of distinct configurations is sharply increasing, which makes the application of a single testbed very difficult. For instance, the number of distinct Android devices is exponentially increasing e. Therefore, it is nearly impossible to test an application on every distinct device in a real environment and, at the same time, provide the best user experience where the underlined application works flawlessly on all other devices.

Sixth, mobile games bear additional inherent features that add extra difficulties. For instance, games involve a lot of graphics and other assets, which substantially increase the loading time. This, in turn, challenges the efficiency of the resource allocation policy. Besides, games have inherent hooks that are intended to make the player play the game again and again Novak This makes it difficult to automate the process of accessing the various passes of the game.

Finally, games bear a psychological factor referred to as fun factor Novak Indeed, even in the case of a bug-free scenario, the game can fail because the players do not feel the fun factor so that their actions are random and not comply with game rules.

Because of its inherent subjectivity and vulnerability from one player to another, it is almost impossible to automate the fun factor in testing.

Due to the above challenges and the lack of effective fully automated testing platforms, mobile app testing is still performed mostly manually, costing the developers and the industry significant amounts of effort, time, and money Novak ; Choudhary et al.

This requires attention from both the research community and practitioners. In this perspective, we present in this paper a new take on a mobile game application testing called MAuto. The latter aims to help the tester to create tests that work with Android games. The tests can then be re-run on any other Android device.

The tool records tests from user-interactions and exports them to Appium framework Appium for playback. When the user performs the recording, MAuto generates a test script that reproduces the recorded events. MAuto then uses Appium framework to perform the replay of the test script task.

To validate the developed MAutol, tests are created with the tool for Hill Climb Racing mobile game and successfully executed. The rest of this paper is organized as follows. The description of the developed MAuto system is reported in Sect.

The traditional test automation pyramid introduced by Cohen is highlighted in Fig. It consists of a three layer-pyramid corresponding to End-to-End E2E test, an Integration test and a Unit test at the top of the hierarchy. The width of the pyramid reflects the number of tests to be written in each layer Knott Traditional test automation pyramid Knott Mobile test automation tools are not yet good enough to support the traditional test automation pyramid.

Besides, mobile devices are armed with a variety of sensors e. We primarily focus on E2E testing because of its criticality. More specifically, the application objects and controls are stored as images, which are then compared to the objects displayed on the screen to identify any potential matching.

Once a match is found, the pre-defined step can be re-executed. Besides, every user-interface UI object, which includes buttons, text boxes, selection lists, icons, among others, has a set of properties that can be used to identify, define or validate the underlying object MacKenzie This provides the tester with useful and powerful tools for GUI-testing.

As a result, the automation engineer achieves a high reusable and good maintainable low-cost script development. Such a method is widely accepted in the field and recognized as the best practice according to test automation. However, it is acknowledged that the image recognition-like technique runs on elaborate and time-consuming pixel-comparison algorithms.

On the other hand, such tests can be fragile if the predefined graphical elements are not carefully chosen. For instance, badly chosen algorithms or algorithm parameters can lead to flaky tests Knott Therefore cautious analysis of the context is needed before the application of image like technique. In this approach, user actions are captured and automated based on their associated x — y coordinates on the screen. This allows interactions with UI elements such as buttons and images present at specific, pre-defined locations in the application UI to be reproduced.

However, if the screen orientation or object layout changes, scripts need to be rewritten. Indeed, the test just blindly executes a given action on a given coordinate so that whenever the screen size varies between devices under testing, the test can be broken down easily Knott Therefore, the approach is rarely applied in practice, and only very few tools provide coordinate-based identification. The key in this approach is to identify the characters displayed on screens, e. However, OCR technology is dependent on the ability to identify the visible text, so that any blurring or screen resolution change may have a negative effect on the identification accuracy.

Also, such tools are not suitable to test user-interface elements that are not visible or are continuously changing. OCR recognition tools tend to be slower than other types of tools because they need to scan the whole screen for the text Knott Therefore such techniques experience significant limits and, thereby, are commonly used in tandem with image-based recognition tools. Native object identification is based, first, on recognizing application object properties in the application code, such as ID , XPath , and, second, testing those elements.

Especially, native object recognition is one of the most widely used types of mobile test automation tools where the UI objects are identified using the UI element tree. With native object recognition, the developer can define the IDs or the locators properly and build very robust tests. The biggest advantage of this approach is that it does not depend on changes in the UI, orientation, resolution or the device itself Knott The identification of programmatic objects makes this technique the most resilient to changing code, and hence quite reliable, although, it requires more effort and programming knowledge.

The basis of this approach is to record screen interactions during manual testing, including every mouse movement, keystroke, and screenshot to be replicated later on. This utility usually comes bundled as a record and a playback tool to enable testers with no programming skills to record and replay the flow of a test case.

The test is primarily used for repetitive testing across various platforms and device models. Since each recording is unique, this automation technique is only meaningful in case of stable applications that do not involve important UI modifications. This concerns mainly quick and easy automation of unchanging flows. In the record stage, the UI is connected to the business logic directly.

When the test is recorded, the signals from the UI are intercepted by the Recording Decorator. Once the signals are stored, the decorator sends the signals to the business logic and the AUT will continue as it would without the decorator.

During the execution phase of the test, the UI is put on the sleep mode. Playback Driver reads the signals from the container and sends them to the business logic. Record and replay using a recording decorator adapted from MacKenzie Besides, in practice, many test automation tools are a combination of the aforementioned types and they are not usually locked into a single object recognition type.

On the other hand, one distinguishes noticeable tools that are of paramount importance for the developers: Appium is an open-source test automation framework that can test native, hybrid and mobile web applications on Android, iOS and Windows platforms. One special feature of Appium is that the developers do not have to modify the application binaries to test the application, because Appium uses vendor-provided automation frameworks. The clients have been written in many major programming languages like Ruby, Python, and Java.

Footnote 5. In terms of software implementation, Appium sets up a server into the host machine. The client, where the test logic is located, connects to the server. If the operating system of the device is Android, the server forwards the commands from the client to the device via UI Automator frame-work see, Fig. SikuliX Footnote 6 : is a tool that automates everything on the screen. It is formally known as Sikuli. Footnote 7 It uses OpenCV image recognition to find the objects to click on the screen.

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Also, the game might change the environment and the context where the object is presented, e. Full size image. Community and Moderator guidelines for escalating issues via new response…. An example of a file created by MAuto in order to save the click coordinates, screenshots and query images is shown in Fig.

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We reviewed the motivation, key milestones and challenges pervading the development of automated mobile game testing tools. Google Robo Test. Yours Locktime Software. The image recognition solution MAuto can therefore easily be extended to an iOS environment.

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Often, the container wraps all the functionality of a game. Reinvents regular expressions and resource locators. The current version of MAuto has a predefined box to crop the query image from the click-coordinate and, sometimes, the box size becomes too small to contain a usable number of features.

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This means the usability of the application in the browser is not the same as in the device without MAuto. Lin, Y. In Fig. Google Scholar Yeh, T.

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