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Postprint
This is the accepted version of a paper presented at 3rd International Workshop on User
Interface Test Automation and Testing Techniques for Event Based Software.
Citation for the original published paper:
Ahmed, B S. (2020)
An Automated Testing Framework For Smart TVapps Based on Model Separation
In: IEEE International Conference on Software Testing, Verification and Validation
Workshops (ICSTW) (pp. 62-73). IEEE Computer Society
https://doi.org/10.1109/ICSTW50294.2020.00026
N.B. When citing this work, cite the original published paper.
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An Automated Testing Framework For Smart TV
apps Based on Model Separation
Bestoun S. Ahmed
Dept. Mathematics & Computer Scie. Karlstad University
Karlstad, Sweden bestoun@kau.se
Angelo Gargantini
Dept. Manage. Info. & Production Eng. University of Bergamo
Bergamo, Italy angelo.gargantini@unibg.it
Miroslav Bures
Dept. Computer Scie. Czech Technical University
Prague, Czech Republic buresm3@fel.cvut.cz
Abstract—Smart TV application (app) is a new technological software app that can deal with smart TV devices to add more functionality and features. Despite its importance nowadays, far too little attention has been paid to present a systematic approach to test this kind of app so far. In this paper, we present a systematic model-based testing approach for smart TV app. We used our new notion of model separation to use sub-models based on the user preference instead of the exhaustive testing to generate the test cases. Based on the constructed model, we generated a set of test cases to assess the selected paths to the chosen destination in the app. We also defined new mutation operators for smart TV app to assess our testing approach. The evaluation results showed that our approach can generate more comprehensive models of smart TV apps with less time as compared to manual exploratory testing. The results also showed that our approach can generate effective test cases in term of fault detection.
Index Terms—Automated testing framework, Software testing, Smart TV apps, Software quality assurance
I. INTRODUCTION
Smart TV application (app) is a software designed to run on smart TV devices. In addition to the television service, smart TVs add many functionalities and the ability to connect to the Internet. Nowadays, smart TVs are going to dominate the television market, and the number of connected TVs is growing exponentially. This growth is accompanied by an increase in the number of consumers and the use of smart TV apps that drive these devices.
In general, software apps testing of smart devices can be considered as an extension and evolution of testing practices from the traditional user interfaces (UI) like the graphical user interface (GUI) of desktop and web apps. The testing practices for these GUIs have been studied extensively in the last decade, and as a result, many sophisticated methods, algorithms, and tools have been developed. Banerjee et al. [1] studied more than 230 articles published between 1991-2013 in the area of GUI testing and Li et al. [2] surveyed the literature in two decades of web app testing. As for smart devices, Zein et al. [3] have studied the mobile apps testing area through 79 articles published between 2005-2015. It is no-ticeable that in the three types of apps (i.e., desktop, web, and mobile apps), model-based testing is the common approach among the leading testing technologies in the literature. We also noticed a lot of overlap among the model-based testing
approaches of GUI. For example, the MobiGUITAR strategy [4] for systematic mobile app testing has been inspired by the GUITAR strategy [5] for desktop GUI testing. Here, the model is constructed using the user interaction to create the interaction events; then the model used to generate the test cases. The finite-state machine (FSM) and directed graphs were used widely in the literature as a model abstraction to represent the state and events on the GUI. Despite these common aspects of the testing, due to the different interaction mode, it is impossible to have a comprehensive strategy for all types of apps. In other words, it is not possible to use the same framework to test mobile, web, and desktop apps.
With the increasing importance and use of smart TV apps, there is an urgent need to have a testing framework for these apps. It is becoming increasingly difficult to ignore the testing of smart TV apps and assuming that they have the same testing procedure that we usually use for mobile apps. While there are many analogies between the two kinds of apps, still there is a great deployment effort estimated to migrate from mobile to smart TV app testing due to the different mode of the user interaction. Although many strategies can be found for mobile apps testing, so far, there has been no discussion about smart TV apps. In fact, far too little attention has been paid to test this kind of app and so far, we cannot find a systematic framework to test it. An essential contribution of this paper is to identify those analogies and differences between the testing procedure for these two types of apps. Recently, we have addressed the key ingredients and challenges for smart TV apps testing [6]. We also proposed some preliminary solutions for those problems.
In this paper, we aim to establish the research foundation of smart TV app testing. To achieve this aim, we developed a black-box model-based testing framework called EvoCreeper to test smart TV apps. Our approach is to explore the GUI of the smart TV app by examining each element and monitoring its reaction. The testing framework takes the TV app to automatically create a complete model by examining the GUI widgets and the transitions among them; then, it generates the test cases based on this model. The framework also supports test case execution and verification. While we developed new algorithms for this testing framework, still, we deployed many algorithmic concepts (wholly or partially) from the earlier
methods and practices used for GUI, web, and mobile apps. The framework constructs a model for a given smart TV app cumulatively by exercising the user interface and extracting the runtime information using the remote control device interac-tion with the app. An important initial stage of the proposed testing framework is the black-box reverse engineering stage that is used to generate a comprehensive model for the app-under-test. The generated model can also be used for several verification and validation activities during the development process.
Another significant contribution of this paper is the idea of test case generation based on sub-model separation. Using the FSM and directed graphs as a model to represent the widgets and events on the GUI leads to a graph with many nodes and edges. The nodes are the widgets on the GUI and the edges are the transition among them. Due to the different nature of smart TV apps, the number of edges and nodes will be much higher than other apps. This will lead to a combinatorial explosion problem in the model in which we cannot generate all the test cases. One possible solution towards the optimization of the test cases is to use search-based algorithms to cover all the nodes and edges by the smallest set of test cases. However, in many instances, these algorithms are computationally expensive for such a complex model. Besides, search-based algorithms are more suitable for comprehensive testing of the app. Also, for this kind of model, where the sequence of the states is crucial, using search-based algorithms may not lead to the correct sequence of events on the smart TV app when it comes to executing the test cases due to the generation of unfeasible transition. As in the case of desktop GUI testing, either the generated test suite must be repaired or a constraint solver must be used. Both solutions are computationally expensive. In this paper, we introduce a new model-based prioritization method to generate customized effective test cases by extracting the user preferred sub-model from the exhaustive model (we call it Mega-model). We have demonstrated the preliminary design of this model constructor in our recently published study [7]. Using this method, more effective and efficient test cases can be generated by giving the authority to the tester to choose the path that he wants to test.
II. MOTIVATION ANDRELATEDWORK
Model-based concepts have been used frequently in the lit-erature to test the GUI of different software apps to assess their functionality. Most of the studies simulated the user interaction behavior with the GUI to construct the model. Here, reverse engineering has been used as an alternative to the classical capture/replay tools [8] to build a model. The model is then used to generate different test cases that represent sequences of execution to be run on the GUI for verification. Typically, the model is presented as nodes and edges. The nodes represent all the building blocks (i.e., widgets) of the GUI, (e.g., buttons, menus) and the edges represent the transitions among the nodes and the GUI’s execution behavior (e.g., click, touch).
Using this approach, Memon et al. [5], [9] developed GUI-TAR, a reverse engineering framework to model the GUI of the desktop software apps. GUITAR generates an event-flow graph (EFG) by capturing the widgets on the GUI. An algorithm extracts all the window’s building blocks of the GUI, their values and properties. The capturing algorithm also captures different widgets that leads to obsolete test cases. For example, when the label widget is not clickable and presenting it in a test case leads to non-executable test. Here, an algorithm has been used in GUITAR to repair those obsolete test cases. This approach has undergone many development stages in different studies. Aho et al. [10] presents an extensive survey for these studies and the techniques.
In line with this approach, Mesbah et al. [11] suggested CRAWLJAX, a web app’s reverse engineering method to generate models that relied on the dynamic crawling on the app to detect the clickable widgets. In addition to the dynamic crawling on the app, CRAWLJAX also relies on the code scanning of the web app to fine-tune the model. The model then used to generate the necessary executable test cases. This approach also motivated other researchers to use it for mobile app model-based testing. Joorabchi and Mesbah [12] developed a reverse engineering method to construct the model for the GUI of mobile apps.
With the different app types and different software tech-nologies, in most cases, the same approach has been applied in the literature. However, it is the technology, app type, and user interaction that makes the difference when applying the approach. Within each app type, there is a set of difficulties that alter this model-based approach to be different. Due to these differences, especially in GUI technology, it is challeng-ing to develop a generic testchalleng-ing framework that can generate models and test cases for different kind of GUI-based apps.
The smart TV app is another new kind of smart device app that relies on a fully interactive GUI. Smart TV devices are becoming more popular these days as there are a lot of advancements in the device and app development. Nowadays, smart TV devices are actively involved in other technologies like smart home, Internet of things (IoT), and controlling other consumer electronics. For example, smart security camera system [13], home sleep care with video analysis using smart TV app [14], controlling the smart home from smart TV [15], Smart Lighting Control [16], healthcare app of smart home [17], home appliances control through Smart TVs [18], [19], and many other apps. It is strongly become difficult to ignore the testing of the smart TV app. Using model-based testing approach for smart TV app is relatively under-explored in the literature.
Although there are a lot of synergies between the mobile and web apps’ testing, still, there are many expected difficulties when one tries to apply the same model-based approach due to the different user interaction with the GUI and the various technologies that the smart TV app build on. Mainly, using the remote control device to interact with the app will change the model. The widgets representation on the GUI and building technology behind that also will change the model of the app.
Applying the same reverse engineering methods used for the web and mobile apps will lead to creating a useless model when it comes to executing the test cases due to the different user interaction. When modeling the web app, the model is prepared for the interaction of mouse and keyboard inputs. In the same way, the model for the mobile app is prepared for the interaction of the touch screen with the fingers for example. However, smart TV app relies on the remote control device which behaves differently. For example, the mouse or finger can move in different directions that lead to moving from any widget to another for an event. Whereas, the remote control device moves only in four directions (Left, Right, Up, and Down).
The user interaction behavior has a direct impact on the type of the model graph generated for testing in the literature. For example, Nguyen et al. [5] constructed the EFG as a model for the desktop GUI testing, whereas, Amalfitano et al. [4] constructed the state machine graph as a model for mobile app testing. For smart TV apps, both the EFG and state machine graph models are not applicable. Here, each move from a node to another on the graph is a step. Hence, the weight of an edge between two nodes on the graph is the same. While this weight of the edge is not essential for the other apps, it is essential in the smart TV app. For example, while the user can move from an icon to another in the mobile app, there are restrictions of movement in the smart TV apps. Here, we assume the regular daily use of the apps. Nowadays, from a typical personal computer (PC) to smart TV, most of the devices are deployed with a touchscreen. However, still the normal daily user interaction with the PC app is the mouse device and with the TV is the remote control device.
Recently, we have tried to study and summarize the key problems of testing smart TV apps [6]. In our study, we have also tried to give different suggestions to solve those problems without implementing them. In another recently published study [7], we tried to construct a preliminary model smart TV apps, without using the model in any testing activities. A significant effort can be found by Cui et al. [20] to address the testing of the smart TV app. The study follows the previous approaches of model extraction by exhaustively explore the Android smart TV app to detect all the GUI widgets then optimize and filter them for the model construction. Here, a significant effort is needed for model optimization and reduction. The widgets extraction used a white-box crawler to scan the code of the app to construct a preliminary model, that contains many obsolete nodes. These obsolete nodes are originated from the detection of all the widgets in the crawling of the code regardless if the widgets are clickable or not. For example, the crawler detects a text label on the GUI as a node in the model while it is not active as it is not clickable. The study proposed an algorithm to reduce those obsolete nodes by deleting the inactive (or obsolete) nodes. Although promising, as mentioned previously, this approach has many drawbacks. When the app size become bigger, this process becomes time-consuming and there is a great possibility to stuck in the combinatorial explosion problem in the constructed model.
Also, there is a need to formulate the model for the complex structure of different apps. The study did not show the detail of the testing process nor named the apps used for the case studies. It is not clear also how the test cases generated from the constructed model and what kind of assessment used to evaluate the effectiveness of the generated test cases.
In contrast to the Cui et al. study [20], our approach uses a more effective method by only detecting the active widgets of the GUI, which in turn does not need to reduce the model. Hence, the constructed model by our approach is a more realistic model and there is no need for an addition algorithm to repair the generated test cases from it. Another contribution of our approach is that it will consider the actual movement of the remote controller device and does not rely on the code scanning of the app. Hence, in contrast to the study by Cui et al., our approach uses the black-box approach in which it will construct the model for the app, even its code is not available. Here, the GUI is explored using the remote control device while examining each widget on it and listen to the reaction for recording. When the widget element is clickable, the algorithm will consider it as a state in the model. The transition among the recorded states then represent the edges in the model.
III. SMARTTV APPTECHNOLOGY ANDITSTESTING
CHALLENGES
As in the case of the other new smart apps nowadays, Software Development Kits (SDK) used to develop smart TV apps. From the development perspective, each smart TV platform used to have its SDK. Cross-platform frameworks like Joshfire1, Mautilus2, and Smart TV Alliance3 were also an option to develop apps. However, the apps developed by them were able to work on a few commercial TV devices. Besides, some of those projects were shut down after a while. Tizen studio SDK nowadays used as an effective development framework that depends on the latest web technology and has a set of supporting tools for development. Tizen studio used to develop apps for the Tizen operating system for smart TV devices. To support cross-platform apps, Tizen used the most up-to-date web packages such as CSS, HTML5, JavaScript and W3C widget.
The user primarily interacts with the smart TV app through the remote control device. Although many smart TV devices are nowadays supporting the direct interaction with the touch-screen, still, the 10-feet (3m) distance is the standard distance of the user from the TV device [21], [22]. In fact, the remote control device is not as easily as used like fingers or mouse device for example. To this end, developers always consider this situation when designing the GUI of smart TV apps. For example, the developers are avoiding the use of text boxes as much as they can when designing the GUI of the smart TV app because entering text is complicated and not user-friendly. There are six essential and constant buttons on the device that
1https://www.joshfire.com/
2https://www.mautilus.com/
any smart TV must have to deal with the GUI. Those buttons are four navigation buttons (Right, Left, Up and Down), Back to navigate back to the previous state on the GUI, and the OK to select an item on the GUI. There are other buttons on the remote devices for some functional reasons such as on/off buttons or a set of (0-9) numbers for channel jumping, but they are not affecting the navigation.
To generate a model for a smart TV app, it is necessary to know the different layouts that an app may follow. Although there is a freedom for the designers to design the layout per the user preferences, smart TV app has a limited number of layouts that have been constructed for the ease of use with the remote control device. For example, Fig. 1 shows three different layouts presented in three different screens of the CineMup smart TV app used for case study later in this paper. To navigate on the GUI of a smart TV app, the remote control device is the conventional way of interaction. The most critical issue here is the non-flexibility of the remote device to navigate from a widget to another due to its "one step" movement. For example, consider the different movement possibilities for the diagram in Fig. 2. Here, this diagram represents the clickable widgets in Fig. 1a. Take the "UPCOMING" button as a start widget for navigation. The navigation to the "POPULAR" button from the "UPCOMING" button in one step of the remote device is not possible because they are not adjacent. Hence, the user must pass through the "TOP RATE" button to reach the "UPCOMING" button.
Now, turning to the "TOP TV" button in Fig. 2, there are three paths to reach this button from the "UPCOMING" button. Those path edges are [A:B:C], [A:G:F], and [D:E:F]. To reach "TOP RATE" from the "UPCOMING," for covering the [A] edge, the user must press the "Right" button on the remote device. In the same way, to cover the path [A:B:C], the user must press the remote device three times (i.e., Right, Right, then Down) to reach the "TOP TV" button. To construct a model that simulates this navigation process for the user, the model construction algorithms for mobile or desktop apps are useless here and there is a need for new algorithms to generate a new model.
IV. THEMODELGENERATIONMETHOD
In this section, we present our automated approach to generating a comprehensive model of the smart TV app. The model is generated with the help of our crawler that visits all the clickable states in the GUI of the smart TV app. Unlike the other crawling algorithms for desktop, web, and mobile apps, our crawling algorithm uses a full black-box crawling by simulating the actual interaction of the user with the app. Hence, there is no need to scan the app source code to generate the model. Also, following this approach, there is no need to repair the generated model for the redundant and obsolete states. By the end of the crawling, our strategy will generate the mega-model that forms a full representation of all widgets and transitions in states (nodes) and edges. We used the smart TV emulator in the Tizen SDK to run our crawler and mega-model generation algorithm. The following
subsections give the detail of the crawling algorithm, the mega-model representation, and the notion of sub-mega-model and mega-model subtraction.
A. The Smart TV App’s Crawler and Its Black-box Mega-model Generation
We have developed an algorithm to crawl the smart TV apps extensively using the remote control device emulator to discover the actually clickable widgets on the GUI. This crawl-ing process aims to construct the mega-model that represents a comprehensive GUI model. We have adopted and developed the concepts used in the literature for desktop and mobile app crawling. In contrast to those approaches, we used the remote control device to explore the GUI directly without going to the details of the internal code structure of the app. Algorithm 1 shows the steps of the crawling method.
Algorithm 1: The Steps of the Smart TV GUI Crawler Algorithm
Input: v1is the starting or user selected state
Input: Itmaxis the max number of crawling actions
Output: List of states to be modeled Lvand the transitions among them 1 Check the focus point
2 if focus not set then
3 Display the message "Please Select the Focus Point"
4 Set v1as focus
5 Set Lvas a new empty lists of states 6 exporeTVApp(v1,Itmax,Lv) 7 return Lv
8
9 function exporeTVApp(s, i, Lv):
Input: s is the starting state
Input: i is max number of actions still to perform Input: Lvis the list of visited states
10 if i = 0 then /* max actions reached */
11 return
12 foreach direction d in {DU p, DDown, DLef t, DRight, DOK} do 13 apply direction d to navigate to a new state currentState
14 monitor emulator log for reaction
15 if currentState.active = true then
16 Record the in/out transitions
17 if currentState 6∈ Lvthen
18 add currentState to Lv
19 exporeTVApp(currentState, i − 1, Lv)
20 Back to the parental state as currentState
As mentioned previously, the smart TV app technology depends mainly on the JavaScript programming language. The starting widget on a GUI of the app depends on the "focus" point specified by the developer in the JavaScript code. The focus point determines the location of the remote control cursor on the GUI. For the majority of the apps we explored, the focus point was not set during the development. Here, when the user opens the app, he must move the cursor using the remote control device to choose the focus point. To this end, our algorithm starts by checking the focus point. If the focus point was not set, then the algorithm will ask the tester to move the cursor and choose the focus point to start.
To explore the app comprehensively, from each newly discovered widget, the algorithm takes five directions DU p, DDown, DLef t, DRight, and DOK to navigate on the GUI. When a new widget is discovered, the algorithm will add
(a) (b) (c)
Fig. 1: Three different screens for the CineMup smart TV app
UPCOMING TOP RATE POPULAR
NEW PLAYING POPULAR TV TOP TV
A B
C D
E F
G
Fig. 2: Representation of the states in the Fig. 1a with the right and wrong state transitions
it to the final list of states that must be represented on the mega-model. The algorithm will continue this exploration until there are no new widgets to discover. The algorithm uses the Back key for moving back to the parental widget and GUI window. Another stopping criterion for this crawling algorithm is the setting of the iteration number at the beginning. For some cloud-based smart TV apps (for example, YouTube), it is impossible to explore the app comprehensively. For example, in the case of YouTube, each video is a state on the model. Here, the list of videos is not finite and the algorithm will continuously discover new widgets. We set an iteration number in the algorithm for this situation. However, for precise and finite results in this study, we did not use such an app in the case studies.
To show the concepts of the crawling algorithm, we consider a running example for one window of the GUI with six widgets as shown in Fig. 3. We consider v1 as a starting widget. The algorithm starts from v1to crawl in four main directions DU p , DDown , DLef t , and DRight for the full exploration of the GUI. For each direction, the algorithm checks for a new widget that can be discovered by listening to the log action of the smart TV emulator. Each new discovered clickable widget becomes a state on the mega-model. For example, from v1, the up and left directions DU p , DLef t will not lead to any widgets. The right direction DRight leads to v2 and the down direction DDown leads to v4. The algorithm then starts from the newly discovered widget, i.e., v2. From v2, the algorithm discovers the new states v3and v5in the DRightand Dd directions. The algorithm also discovers v1 in the DLef t direction; however, it is neglected by the algorithm as it is already discovered. The algorithm will continue until it cannot find more widgets.
To consider all the directions by the algorithm to deeply
Direction New Widgets v1 + Du v1 + Dd v4 v1 + Dl v1 + Dr v2 Direction New Widgets v2 + Du v2 + Dd v5 v2 + Dl v1 (Rep. Neglect) v2 + Dr v3 v4 + Du v1 (Rep. Neglect) v4 + Dd v4 + Dl v4 + Dr v5 (Rep. Neglect) Direction New Widgets v3 + Du v3 + Dd v6 v3 + Dl v2 (Rep. Neglect) v3 + Dr v5 + Du v2 (Rep. Neglect) v5 + Dd v5 + Dl v4 (Rep. Neglect) v5 + Dr v6 (Rep. Neglect) v1 App name Menu v2 v3 v4 v5 v6 v1 App name Menu v3 v5 v6 v1 App name Menu v2 v4 v6 Du Dd Dl Dr v2 Du Dd Dl Dr v3 Du Dd Dl Dr v5 Du Dd Dl Dr v4 Du Dd Dl Dr
Fig. 3: A Graphical Representation of the Smart TV GUI Crawling Right Left Up Down A Left Right Up Down B Left Up Down Right C Up Down Left Right D Up Down Right Left E Up Down Right Left F Right Down Left Back C1 Left Right Up Down C2 Right Left Up Down C3 Left Up Down Right C4 OK Right Down Left C3-1 Left Right Up Down C3-2 Right Left Up Down Left C5 C3-3 OK Back A B C
Fig. 4: Full crawling of the GUIs in Fig. 1
discover an app with multiple windows, we also consider the OK and Back keys. Fig. 4 shows a pilot deep crawling example for the smart TV app with three GUIs in Fig. 1.
We model the GUI of the smart TV app as a multigraph G = (N, E, s, t), where N is a set of nodes, N 6= ∅, and E is a set of edges. Here, s : E → N assigns each edge to its source node and t : E → N assigns each edge to its target node. One start node ns ∈ N is defined when the focus point set. The set Ne⊆ N contains the end nodes of G, Ne6= ∅. N represents the widget elements of the GUI of the smart TV app. E represent the possible transitions between the widget elements (N ) using the remote control device. The widget here is the clickable widgets on the GUI. As previously mentioned, the crawler, for example, will not detect the label
widgets on the GUI to be considered in the mega-model. By following this approach, we can avoid a great testing overhead time needed to repair the model and the obsolete generated test cases when all widget considered. Hence, the constructed mega-model represents a real user interaction with the app and all the test cases generated using this model can be executed directly.
B. The Notion of Sub-model and Model Subtraction
Due to the many paths and ways of reaching the widgets in the smart app and the transitions among them, most probably, the mega-model will contain many states and edges. The mega-model becomes a complicated model with many edges and nodes. To assure the successful execution of a generated test case, it must start from the starting state of the graph and goes to a specific end state. Here, a test case is a sequence of steps to pass several nodes and paths to reach a final destination that usually leads to an action on the app. Hence, a step is a node or a state on the mega-model graph and the edge between two nodes is the move action by the user with the remote control device.
As the end nodes are representing the final destination for some action on the app, a natural way to generate a set of test cases is to consider all the end destination nodes on the mega-model and generate paths from the start to the end nodes. Although feasible, this test generation approach is exhaustive and computationally expensive. Heuristic search-based algorithms is an approach to generate and optimize test cases. However, it is more suitable to test the whole app not a specific function on the app. Besides, generating all the possible paths for the mega-model will lead to run many impractical test cases which may not be needed for testing a specific function on the app. Also, the search-based algorithm becomes complicated and computationally expensive for large mega-models and there is a need for specially designed data structures.
In this paper, we introduce the notion of sub-model to generate the test cases. Here, we generate the test cases based on the selected functions that we want to test. A function is an expected output after running a sequence of steps on the app. For example, the "Play Trailer" in Fig. 9c is a destination node in the app that is used for "play" function of a specific movie trailer track. Following this approach, we can test more specific functions and generate more effective test cases. Also, as the models are sub-models and they are relatively simpler than the mega-model, we can generate the test cases with less computation time and resources. As an example, We identified thee sub-models on the mega-model in Fig. 5.
As shown in Fig. 5, we highlighted the sub-models on the mega-model in different colors (Red, Blue, and Green). Each color is a directed graph with start and end nodes that represent a complete model with all related nodes and edges to fulfill a specific action. Our testing strategy separates these sub-models based on the user selection of the end nodes. For example, Fig. 6 shows the graphs for the (Red, Blue, and Green) sub-models
START UPCOMING_0 NOWPLAYING_1 TOPRATED_2 POPULARTV_3 TOPTV_4 POPULAR_5 PlanetEarth_6 GravityFalls_7 CosmosAPersonalVoyage_8 AvatarTheLastAirbender_9 BandofBrothers_10 BlackMirror_11 TheSopranos_12 MyHeroAcademia_13 Sherlock_14 DeathNote_15 BreakingBad_16 SteinsGate_17 TheWire_18 OvertheGardenWall_19 TheJinxTheLifeandDeathsofRobertDurst_20 StrangerThings_21 FullmetalAlchemistBrotherhood_22 Life_23 PlanetEarthII_24 RickandMorty_25 PlayTrailer_26 ShowImages_27 Close_28 end_29 end_30 PlayTrailer_31 ShowImages_32 Close_33 end_34 end_35 PlayTrailer_36 ShowImages_37 Close_38 end_39 end_40 PlayTrailer_41 ShowImages_42 Close_43 end_44 end_45 PlayTrailer_46 ShowImages_47 Close_48 end_49 end_50 PlayTrailer_51 ShowImages_52 Close_53 end_54 end_55 PlayTrailer_56 ShowImages_57 Close_58 end_59 end_60 PlayTrailer_61 ShowImages_62 Close_63 end_64 end_65 PlayTrailer_66 ShowImages_67Close_68 end_69 end_70 PlayTrailer_71 ShowImages_72 Close_73 end_74 end_75 PlayTrailer_76 ShowImages_77 Close_78 end_79 end_80 PlayTrailer_81 ShowImages_82 Close_83 end_84 end_85 PlayTrailer_86 ShowImages_87 Close_88 end_89 end_90 PlayTrailer_91 ShowImages_92 Close_93 end_94 end_95 PlayTrailer_96 ShowImages_97 Close_98 end_99 end_100 PlayTrailer_101 ShowImages_102 Close_103 end_104 end_105 PlayTrailer_106 ShowImages_107 Close_108 end_109 end_110 PlayTrailer_111 ShowImages_112 Close_113 end_114 end_115 PlayTrailer_116 ShowImages_117 Close_118 end_119 end_120 PlayTrailer_121 ShowImages_122 Close_123 end_124 end_125 FiftyShadesFreed_126 ThePost_127 Zootopia_128 JohnWick_129 15IQKrachoot_130 GuardiansoftheGalaxy_131 JusticeLeague_132 TheAvengers_133 TheShapeofWater_134 BeautyandtheBeast_135 Sleight_136ThorRagnarok_137 TombRaider_138 TheMazeRunner_139StarWarsTheLastJedi_140 PitchPerfect3_141 AQuietPlace_142 ReadyPlayerOne_143 BlackPanther_144 Coco_145 PlayTrailer_146 ShowImages_147 Close_148 end_149 end_150 PlayTrailer_151 ShowImages_152 Close_153 end_154 end_155 PlayTrailer_156 ShowImages_157 Close_158 end_159 end_160 PlayTrailer_161 ShowImages_162 Close_163 end_164 end_165 PlayTrailer_166 ShowImages_167 Close_168 end_169 end_170 PlayTrailer_171 ShowImages_172 Close_173 end_174 end_175 PlayTrailer_176 ShowImages_177 Close_178 end_179 end_180 PlayTrailer_181 ShowImages_182 Close_183 end_184 end_185 PlayTrailer_186 ShowImages_187 Close_188 end_189 end_190 PlayTrailer_191 ShowImages_192 Close_193 end_194 end_195 PlayTrailer_196 ShowImages_197 Close_198 end_199 end_200 PlayTrailer_201 ShowImages_202 Close_203 end_204 end_205 PlayTrailer_206 ShowImages_207 Close_208 end_209 end_210 PlayTrailer_211 ShowImages_212 Close_213 end_214 end_215 PlayTrailer_216 ShowImages_217 Close_218 end_219 end_220 PlayTrailer_221 ShowImages_222 Close_223 end_224 end_225 PlayTrailer_226 ShowImages_227 Close_228 end_229 end_230 PlayTrailer_231 ShowImages_232 Close_233 end_234 end_235 PlayTrailer_236 ShowImages_237 Close_238 end_239 end_240 PlayTrailer_241 ShowImages_242 Close_243 end_244 end_245 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Fig. 5: The Mega-model with Sub-models Highlighted for the CineMup Smart TV app
in Fig. 5. The following section shows the test generation and execution algorithm for the sub-models.
V. THETESTGENERATIONSTRATEGY
To generate the test cases for the smart TV app from the sub-model graph, we introduce a testing criteria, All Edge Coverage, that requires to cover all the edges between the nodes to execute all transitions from a widget to another. A test case t is a sequence of nodes n1, n2, .., nn, with a sequence of edges e1, e2, .., en−1, where ei= (ni,ni+1), ei ∈ E, ni∈ N . Further on, n1= ns, and nn ∈ Ne. The Test Set T is a set of test cases. T achieves the All Edge Coverage criterion if each edge e ∈ E is traversed at least one by a test case in T . The Algorithm 2 shows the pseudocode of the used test generation algorithm.
Algorithm 2: The test generation algorithm from the sub-model
Input: A sub-model Graph (G) Output: The test set T
1 T ← ∅, COV ER ← ∅, ALL ← ∅
2 for e ∈ E do // build the COV ER set with all the edges to be covered
3 let n1and n2be the head and tail of e, where {n1, n2} ∈ N , e ∈ E; 4 COV ER ← COV ER ∪ {(n1, e, n2)}
5 for c ∈ COV ER do // build all the useful paths that can be traversed
6 Let z be a path in G starting with ns∈ N and ending with any ne∈ Ne
that covers c;
7 ALL ← ALL ∪ {z};
8 while COV ER 6= ∅ do // select the best paths as tests 9 best ← a path from ALL, which contains maximal number of elements
from COV ER as its sub-paths ;
10 T ← T ∪ best;
11 Let x be the set of edges of best;
