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Ahmed, B S., Bures, M. (2019)
EvoCreeper: Automated Black-Box Model Generation for Smart TV Applications
IEEE transactions on consumer electronics, : 1-11
https://doi.org/10.1109/TCE.2019.2907017
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EvoCreeper: Automated Black-Box Model
Generation for Smart TV Applications
Bestoun S. Ahmed, and Miroslav Bures
Abstract—Smart TVs are coming to dominate the television market. This accompanied by an increase in the use of the smart TV applications (apps). Due to the increasing demand, developers need modeling techniques to analyze these apps and assess their comprehensiveness, completeness, and quality. In this paper, we present an automated strategy for generating models of smart TV apps based on a black-box reverse engineering. The strategy can be used to cumulatively construct a model for a given app by exploring the user interface in a manner consistent with the use of a remote control device and extracting the runtime information. The strategy is based on capturing the states of the user interface to create a model during runtime without any knowledge of the internal structure of the app. We have implemented our strategy in a tool called EvoCreeper. The evaluation results show that our strategy can automatically generate unique states and a comprehensive model that represents the real user interactions with an app using a remote control device. The models thus generated can be used to assess the quality and completeness of smart TV apps in various contexts, such as the control of other consumer electronics in smart houses.
Index Terms—Model generation, Smart TV application, Ap-plication reverse engineering, Model-based testing.
I. INTRODUCTION
The smart TV is a modern technological device that is a hybrid of a computer and a traditional television. In addition to a conventional TV terminal, this device incorporates digital content and an operating system (OS) with an Internet con-nection. Smart TVs usually provide access to broadcast media, games, digital services, various online interactive sessions, on-demand entertainment, Internet browsing, and many other services, and these devices are expected to become even more intelligent, interactive, and useful in the future [1]. Invest-ments in related technological advanceInvest-ments by electronics companies and IT firms have recently been increasing. As a result, new terminals and applications for smart TVs have been launched. It is expected that these devices will soon become a common feature of smart homes within an Internet of Things (IoT) context [2]. This explains why the smart TV market had grown to be worth $265 billion by 2016 [3].
Like all new smart devices, a smart TV is operated by an OS that handles the necessary hardware interaction functionality and a set of applications (apps) installed on the OS to provide various services to the user. Despite the visual similarities
This study is conducted as a part of the project TACR TH02010296 “Quality Assurance for Internet of Things Technology”.
B. Ahmed is with the Department of Mathematics and Computer Science, Karlstad University, Sweden and the Department of Computer Science, Czech Technical University, Karlovo nam. 13, Prague, Czech Republic, email: bestoun@kau.se
M. Bures are with the Department of Computer Science, Faculty of Electrical Engineering, Czech Technical University, Karlovo nam. 13, Prague, Czech Republic
between smart TV apps and mobile apps, the mode of user interaction with smart TV apps is different. For mobile apps, the user interacts with the touchscreen of the device (i.e., the apps) directly by hand, whereas for smart TVs, the user inter-acts with an app through another device, namely, the remote controller. Of course, some vendors also provide touchscreen interactions to users. Additionally, some recent studies have investigated gaze-based interactive interface design for smart TV apps [4]. However, the way in which a smart TV behaves is still primarily based on a remote control device when it comes to the navigation of the user interface (UI) states. Moreover, the user of any TV (including smart TVs) is usually far away from the screen and uses the remote controller to operate apps almost all the time.
With the increasing number of smart TV apps, there is an urgent need for methods of modeling UIs based on user interactions. As in the case of the UIs of desktop or mobile apps, such a model can be used for many purposes. It can be used to assess the completeness of an app during development or to assess the quality of the app during testing. Such a model can also be used to trigger requirement specifications and analyze poorly documented legacy apps. It can also be used in the smoke testing of apps. In fact, most developers currently create mental models of UIs in order to better comprehend the software.
Creating a model of the UI via the user interaction mode is a common first step in assessing the quality and completeness of a UI-based app. While model generation is a common step, the presentation of the model depends mainly on the type of app and the user interaction mode. For a smart TV app, if the app is to be used on a touchscreen TV, the same state-of-the-art model generation methods used for mobile apps may also be useful when deployed in the smart TV operation environment. However, there is still a need to design a model generation method for gaze interaction with an app, and our method may not be useful for this purpose. Thus, in general, the model of a smart TV app does not look like the model of a mobile app due to the different mechanism of transitioning among the UI states. In a mobile app, a user can easily transition among app states by going directly from one state to another. However, for example, in a smart TV app, when the user wants to go from one state to another, he must pass through some set of states by means of the remote control device in order to reach the destination state.
It is possible to create a model using the same state-of-the-art approaches used for the reverse engineering of mobile apps. In practice, however, that model will be useless for a smart TV app. Normally, a model is created for some specific purpose. For example, we may create a model to generate several test
cases. However, it will be impossible to run those test cases on a real smart TV because there will be significant differences in the transitions among the states of the UI.
In this paper, we propose a strategy called EvoCreeper1 for the automatic creation of comprehensible UI models for smart TV apps. EvoCreeper is a generic open-source model generator for smart TV apps that can generate directed graphs by reverse engineering an app without knowledge of its internal code structure. In this strategy, the UI states and the transitions among them are examined in order to create the model. The contributions of this paper can be summarized as follows:
• We present a new comprehensive automated black-box strategy in which a smart TV app is executed on an emulator and information is extracted from the UI during runtime.
• We present an efficient exploratory algorithm that can ex-plore the elements of a UI by simulating user interactions with a smart TV app.
• We illustrate the implementation of our strategy within our EvoCreeper tool, which can create directed graph models of smart TV apps. The models thus generated can be used for various development and testing purposes.
• We have developed an algorithm to verify the correctness
of the generated models.
• We have empirically evaluated our strategy through four real case studies.
The rest of this paper is organized as follows. In Section II, we summarize the technological background of smart TV apps. This section also mentions the related challenges and the most closely related works in the literature. Section III summarizes the details of our approach for generating models of smart TV apps. Section 4 presents a proof of concept for our model generation approach. Section V reports the evaluation results, and Section VI summarizes the threats to the validity of our evaluation experiment. Finally, Section VII gives concluding remarks and discusses our future research directions.
II. BACKGROUND ANDRELATEDWORK
Smart TV apps are developed using software development kits (SDKs). Each platform has its own SDK for the de-velopment of software for TV devices. For example, the Android and Tizen SDKs can be used for smart TV app development. Recently, a few SDKs have also begun to support cross-platform development. For instance, the Mautilus [5] Smart TV SDK is an example development framework, but at present, the apps developed in this framework work on only some versions of the supported devices. The Smart TV Alliance [6] was another project for supporting cross-platform development; however, this project has been inactive for some time. In fact, the Tizen SDK is currently the SDK that is most commonly used since it provides a set of tools and frameworks for the development of smart TV apps through Tizen Studio that utilize the latest web technologies, such as JavaScript, CSS, HTML5, and W3C widget packaging, which are used by
1EvoCreeper can be downloaded here: https://bit.ly/2StQeG4
most smart TV apps. Additionally, JavaScript is used in most apps as a standard programming language for programming their behavior. The use of JavaScript endows an app with the page jumping capability. It also enables the developer to code complex expressions and calculation structures such as conditional branches and loops.
In general, a smart TV app can be of one of two types: installed or cloud-based. An installed TV app is a stand-alone app installed on a smart TV without the need for an Internet connection, while a cloud-based TV app primarily acts as an interface between the cloud and the TV and offers only shallow content (almost no additional functionality) when there is no Internet connection available.
Regardless of the visual appearance of these apps, mobile and smart TV apps are different in several significant respects. For example, the size of the screen can affect the layout of an app. Smart TVs have wider screens than small mobile devices do. The background color of a smart TV app may also be different from the corresponding color on mobile devices. The size of the icons could also be different. From the perspective user interaction, smart TV apps typically involve less text entry because of the difficulty of entering text using a remote control device. Most smart TV apps are designed to retrieve content from the Internet, whereas this is not the case for mobile apps, which can be standalone apps without Internet connection interfaces [7]. The typical smart TV app is much more straightforward than the typical mobile app, especially in its design layout. We have explained these analogies and differences in detail in our previous study [8].
The way in which the user interacts with the app constitutes an essential difference between smart TV and mobile apps. The user of a mobile app interacts directly with the app without an intermediate device, while for a smart TV app, the user interacts with the help of a remote controller. In fact, the UIs of smart TV apps are sometimes called 10-foot UIs since 10 feet (3 m) is the standard distance between the user and the TV. Developers consider this distance when developing smart TV UIs [7], [9]. Using a remote control device at this distance is not a user-friendly or responsive experience. Hence, the design of a smart TV UI must consider this significant difficulty.
Navigation in a smart TV app is achieved through a remote control device. Although some new TV devices offer the ability for the user to directly interact with the screen, the most common form of interaction with a TV device is still through a remote control device. A remote control device includes four essential navigation buttons: Right ⇒, Left ⇐, Up ⇑ and Down ⇓. Additionally, a remote control device has an OK button to choose any selected item in an app after navigating to it and a Backbutton ←- to navigate back to the previous screen. These six key buttons should work properly when using an app.
In addition to these six buttons, there are many other buttons on remote control devices that vary from one TV brand to an-other depending on the level of the functionalities they access. Some of them are related to the hardware functionalities of the TV itself. For example, the power button turns the TV on and off. There are typically also ten number buttons (from 0 to 9) for channel jumps and entering numbers in text fields if necessary.
The UI layout of any app plays a primary role in (black-box) model construction. A better understanding of this layout can lead to a more accurate model. Smart TV apps typically follow one of a limited number of layout patterns. Figure 1 shows the three main patterns followed by most smart TV apps. Of these, layout (b) is most commonly used since it presents many items on a single screen.
The remote control device places constraints on the navi-gation from one state to another because it supports only one step of navigation at a time. Hence, each move in the layout is a step. Accordingly, the transition from one state to another is not smooth as in mobile or desktop apps. To move from one state to another in a smart TV UI, the user may need to pass through several other states before reaching the desired state. For nonadjacent states, more than one step is required to move from one to the other. As a simple example, we consider the UI of a puzzle game smart TV app, as shown in Figure 2.
Clearly, there are 12 items (each regarded as a state) in the UI of the app in Figure 2. For clearer illustration, we have numbered these states. For instance, state 1 is the starting state. From here, there are only two valid moves: Right to state 2 or Downto state 3. Note that the user cannot jump directly from state 1 to state 4; we consider this to be an invalid move (red arrow). Hence, to move from state 1 to state 4, the user must press Down twice, passing through state 3.
Generally, model generation for UI-based software apps is performed frequently for many purposes during the develop-ment process, as described in the literature. Such models have been used in the literature for testing and quality assurance purposes (e.g., [10]–[14]). Regardless of the app type, as long as the model is for the UI, it must simulate the users interaction with the app in some form. Using this approach, Memon et al. [15], [16] proposed a reverse engineering technique called GUITAR for modeling the UIs of desktop apps. The technique starts from the main window and automatically captures UI widgets to construct an event-flow graph model. That model is then used to generate test cases for program testing. This technique has undergone considerable develop-ment in many subsequent research papers. Aho et al. [17] presented an extensive survey of those studies and techniques. This approach has also been used for model generation for mobile apps. For example, Joorabchi and Mesbah proposed a reverse engineering technique for generating models of mobile apps, and Amalfitano et al. [18] developed the MobiGUITAR strategy for creating models of mobile apps.
Mesbah et al. [19] also proposed a reverse engineering technique called CRAWLJAX for generating models of web apps. However, this technique relies on a dynamic crawler acting on a web app and detecting the clickable states. The model thus generated can then be used to generate test suites and analyze the app. In fact, this technique works like a gray-box technique rather than a black-gray-box technique because it also scans the code of the app.
Gimblett et al. [20] tried to define a generic approach for establishing models of interactive software to simulate user actions. It is clear from the literature that most related techniques follow the same basic principles. However, the technology, application type, and user interaction mechanism
introduce differences and thus pose challenges for model generation.
As another class of smart devices, smart TVs are currently becoming increasingly popular due to the rapid development of apps that can be installed on these devices and the ability to control other consumer electronics and connect with them in the IoT context. Some examples of the possible applications of such technology include controlling home appliances through smart TVs [21], [22], home sleep care with video analysis using a smart TV app [23], controlling smart homes from smart TVs [24], healthcare applications for smart homes [25], smart lighting control [26], and smart security camera systems [27]. With the increasing prevalence of these applications, it is becoming increasingly difficult to ignore the unique requirements of quality assessment for smart TV apps and assume that the same quality assessment procedures can be used as for mobile apps. As with other smart devices, creating a model of a smart TV app is the first step towards quality assessment.
To create a model for a smart TV app, it is necessary to detect the active states of the app and the transitions among them. While the same basic principles and concepts are followed as in previous approaches to detect new states and state transitions, as mentioned previously, there are significant differences due to the different technologies and modes of user interaction. In desktop GUIs and web apps, the combination of a keyboard and mouse is still the standard mode of user input for interacting with these apps. However, this is not the case for mobile apps because the user interacts with the touchscreen of the device by means of his or her fingers, and hence, different users will exhibit different interaction behaviors. Although this issue has led to the development of new models for mobile apps, many of these strategies still benefit, wholly or partially, from the earlier methods and practices established for the reverse engineering of desktop and web apps. Nevertheless, the differences in interaction create many obstacles and difficulties. For example, Nguyen et al. [16] used an event-flow graph (EFG) as a model of the UI of a desktop app, whereas Amalfitano et al. [18] used a state machine as a model for a mobile app due to the different natures of their interactions. For smart TV apps, neither EFG nor state machine models are applicable. In an app of this kind, each transition from one state to another is, in practice, just one step, while this is not the case in other apps. For example, in a mobile app, the distance between two icons (states) is irrelevant to the transition, whereas this is a critical issue in a smart TV app, and this difference will lead to a different model.
A significant effort to formulate such a model has been made recently by Cui et al. [28]. In their study, a hierarchical state transition matrix (HSTM) was proposed as a model for an Android smart TV app. While this model is promising, there is a need to further develop and formulate it for the complex structures of different apps. However, this type of model is useful when model optimization and reduction are needed. In fact, Cui et al. [28] used a white-box crawler approach to scan the code of an app to construct a preliminary model. This initial model contains many obsolete nodes because the
(a) (b) (c)
Fig. 1: Three main layout design patterns for smart TV apps [7]
1 2
3
4
Fig. 2: A puzzle game smart TV app to illustrate the valid and invalid move based on the remote device
crawler detects all views present in the UI code. Even when a view is not an active state in the UI, the crawler algorithm considers it as a node in the model. For example, a piece of text in an Android app is a view; however, in practice, it is not a clickable state. When this approach is adopted, there is a possibility of combinatorial explosion in the resulting model. Therefore, the authors proposed an algorithm for reducing and optimizing the model by distinguishing obsolete and active nodes. However, this could be a time-consuming task and may lead to deviation from the primary research focus while also adding overhead to the model construction process.
In contrast to the contribution of Cui et al. [28], our approach does not scan the app code. Our EvoCreeper strategy instead explores the UI of a smart TV app by examining each element and observing its reaction. When an element is clickable, the strategy will consider it as a state in the model. Section III presents the details of our strategy.
III. OURMODELCONSTRUCTIONSTRATEGY
In this section, we present our new strategy for automat-ically generating a model of a smart TV app. Our strategy has been implemented with the Tizen SDK, which includes a smart TV emulator; however, the proposed framework is a general one, and it can be applied in combination with other possible emerging SDKs in the future. The strategy depends on the black-box approach to model generation and does not require knowledge of the internal structure of the app code.
The smart TV app is modeled as a directed graph G = (N, E), where N is a set of nodes, N 6= ∅, and E is a set of edges. E is a subset of N × N possibilities. In the model, we define one starting node, ns∈ N . The set Ne⊆ N
contains the end nodes of the graph, where Ne 6= ∅. Each
node corresponds to a UI element (state) of the app. Each edge corresponds to a possible transition between the states focused on by the cursor. These transitions can be triggered
by individual keys on the remote control device. In this study, e ∈ {Dup, Ddown, Dlef t, Dright, DOK} for each e ∈ E.
If the model allows parallel edges, formally being a directed multigraph G0 = (N, E, ns, Ne, s, t), such that N 6= ∅ is a
finite set of nodes, E is a set of edges, s : E → N assigns each edge to its source node and t : E → N assigns each edge to its target node. The node ns ∈ N is the initial/start
node of the graph G and Ne= {ne| ne∈ N has no outgoing
edge } defines nonempty set of end nodes of graph G0. In this study, approach we decided to base the app model on G, which practically does not allow parallel edges, to have the proposal in accord with the current path-based testing approaches, for instance, [29]–[31].
To detect all the necessary states in the app UI for presen-tation in the model, we have developed an algorithm called EvoCreeper. At present, the concept of state detectors for the UIs of mobile, desktop, and web apps is rather famil-iar. However, such detectors differ for each app category. As mentioned earlier, algorithms called crawlers have been developed that can crawl a UI and detect states. Because we are following a new approach to state detection, we do not call our algorithm a crawler. Rather, from a linguistic point of view, the name ”creeper” is perfectly suited to what the algorithm does, whereas the word ”crawler” carries a different meaning due to its use in web and search engine technologies. Algorithm 1 shows the steps of the EvoCreeper algorithm.
One of the problems that EvoCreeper encounters when exploring a UI is establishing the position of the naviga-tional cursor. Technically speaking, from the perspective of a JavaScript developer, this problem arises when a focus point is not set in the app. For several apps on the store, no focus point has been set by the developers. As a result, when such an app runs on the emulator, there are no preselected states in its UI. Instead, the user must use the remote control device to choose a state. Hence, a starting point for the navigator
Input: v1is the starting or user selected states
Output: List of states to be modeled Lv
Iteration It ←1
Maximum Iteration Itmax← max
while ((It < Itmax) k (newV iew 6= null)) do
Use v1 as a start point
From v1 generate five possible directions DU p ,
DDown , DLef t , DRight, and DOK
foreach direction D do Navigate a step
Monitor emulator log for reaction if newState = Active then
if the state is not duplicate then Add newState to Lv
else
Back to the parental state end
Record the in/out transitions end
end It + + end
end
Algorithm 1: EvoCreeper Steps
is missing. This problem is common with cloud-based smart TV apps because the UI changes in real time with the cloud content. Therefore, our strategy starts by checking the initial cursor condition. If a focus point is not set in the app, the strategy starts by asking the user to choose at least one state in the UI from which to start. From this state, the creeper will start creeping the UI evolutionarily and incrementally. If a focus point is already set, the strategy will proceed without asking the user for input.
The algorithm has five directions DU p, DDown, DLef t,
DRight, and DOK in which it can move from each state. When
a new state is discovered in each direction (i.e., newState = Active), the algorithm will add it to the list of states to be modeled, Lv. The algorithm will continue until no new states
are discovered. At this stage, the algorithm will choose to move back to the parent state. As an alternative stopping criterion, the algorithm will perform some preset number of iterations to avoid the possibility of an endless discovery loop that is encountered in some special cases of cloud-based apps. In the post-processing phase, states Lv are converted to
nodes of the app model N . In this phase of the strategy, we used the direct mapping between the states and N , practically speaking, for each l ∈ Lv, an original n ∈ N is created.
Figure 3 shows the directed graph model constructed by our strategy for the CineMup smart TV app. To illustrate the detail of the generated model, Figure 3 also shows a snapshot of three nodes in the graph. Here, each node has a unique identifier number preceded by the actual name of the state on the smart TV app. The model also records the actual transitions that can be performed from a state to another. Each transition name is preceded by the name of the state that is originated from.
The app model G can be subsequently used for two principal
purposes: (1) model checking, where a potential design sub-optimalities can be detected, and (2) automated path-based test case generation, being the major expected use case. Here, the goal is to generate a set of test cases T that satisfy a defined test coverage criteria and counted optimal by a test set optimality criteria.
Respecting the standard approaches used in the field, a test case t ∈ 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. The test case t starts with the start node ns(n1= ns)
and ends with a G end node (nn ∈ Ne) [29], [30]. Test
coverage criteria determine the strengths of the T in the sense of number of alternative paths that are toured in the app during the tests and vary from low test coverage levels (node coverage or edge coverage) to very intense levels as all paths coverage [29]. For the test set optimality criteria, several options can be employed, for instance, the number of test cases, |T |,
average length of the test cases, |t| = 1 |T |
|T |
X
i=1
|ti|, ti ∈ T ,
or total length of a test set l =
|T |
X
i=1
|ti|, ti ∈ T [30], [31].
Such automated generation of test cases, which is subject of other studies, for instance, [30]–[34] open variety of options for construction of automated test frameworks for Smart TV apps and document the applicability potential of our strategy. In the following section, we present an example as a graphical proof of concept for Algorithm 1.
IV. PROOF OFCONCEPT
In this section, we present a proof of concept for the EvoCreeper concept introduced in Algorithm 1. Here, we consider a cloud-based app as a pilot example because this is the most difficult scenario. As shown in Figure 4, each active window has 12 states, and when the user shifts down or to the right, new states may appear. We consider three iterations of the algorithm. We assume that the user will choose v1 as
the starting state. In fact, v1 is the worst-case choice among
the states; we observe that choosing the state in the middle of the window instead may lead to fewer iterations and better recognition of the states. From v1, the algorithm will consider
four main directions: DU p, DDown, DLef t, and DRight. For
this proof of concept, we do not consider DOK because we are
interested in the exploration of the current window, whereas DOK will probably take the algorithm to another window.
In each direction, the creeper algorithm will check for new states, which are most likely new elements in the UI. Considering the first iteration and starting from v1, the up and
left directions, Du and Dl, do not lead to new states, while
the right direction, Dr, leads to v2, and the down direction,
Dd, leads to v5. In the next iteration, the algorithm will start
from the newly discovered states (here, v2 and v5). From v2,
the new states v3 and v6 are identified by the algorithm. In
addition, v1 is discovered in the Dl direction; however, this
state is ignored by the algorithm since it is already included in the state list. From v5, the states v1, v9, and v6 are discovered
in the three directions Du, Dd, and Dr, respectively; however,
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Fig. 3: The constructed directed graph model by EvoCreeper for the CineMup
v1 Du v1 Dd v5 v1 Dl v1 Dr v2 v1 Du v1 Dd v5 v1 Dl v1 Dr v2 v2 Du v2 Dd v6 v2 Dl v1 v2 Dr v3 v5 Du v1 v5 Dd v9 v5 Dl v5 Dr v6 v2 Du v2 Dd v6 v2 Dl v1 v2 Dr v3 v5 Du v1 v5 Dd v9 v5 Dl v5 Dr v6 v3 Du v3 Dd v7 v3 Dl v2 v3 Dr v4 v6 Du v2 v6 Dd v10 v6 Dl v5 v6 Dr v7 v9 Du v5 v9 Dd v13 v9 Dl v9 Dr v10 v3 Du v3 Dd v7 v3 Dl v2 v3 Dr v4 v6 Du v2 v6 Dd v10 v6 Dl v5 v6 Dr v7 v9 Du v5 v9 Dd v13 v9 Dl v9 Dr v10 v1 v2 v3 v4 v5 v6 v7 v8 v9 v10 v11 v12 v1 v3 v4 v6 v7 v8 v9 v10 v11 v12 v1 v2 v4 v5 v7 v8 v10 v11 v12 Du Dd Dl Dr v2 Du Dd Dl Dr v3 Du Dd Dl Dr v6 Du Dd Dl Dr v9 Du Dd Dl Dr v5 Du Dd Dl Dr
Fig. 4: Proof of concepts of the EvoCreeper
The third iteration similarly starts from the newly discovered states, v3, v6, and v9. In the same way, considering all four
directions from each state and filtering out repeated states, four new states are identified: v4, v7, v10, and v13.
EvoCreeper works in an iterative evolutionary manner to discover new states and events in the app. As mentioned, a cloud-based app is considered in this pilot example. Hence, there is no expectation that the app will have a finite number of states. Consequently, our proposed alternative stopping criterion could be useful here. The creeper algorithm will stop after a certain number of iterations or when no new states are discovered.
V. EMPIRICALEVALUATION
To assess the effectiveness of our model generation strategy, we conducted a case study on four smart TV apps. During this evaluation, we attempted to address the following research questions (RQs):
• RQ1. Is EvoCreeper capable of exploring and identifying
the states and transitions of a given smart TV app accurately relative to manual exploration?
• RQ2. To what extent is the generated model complete in terms of the numbers of states and edges? Is the created graph valid?
• RQ3. What is the performance of EvoCreeper compared to that of manual exploration for a given smart TV app?
A. Experimental Objects
Research in the area of smart TV apps is in an early stage. More time may be needed for developers to create and publish smart TV apps, as it is a new development environment. As a result, not many apps and repositories are available for benchmarking. Tizen does maintain a page with several simple apps and examples2. However, most of the provided samples are simple apps with few states. To demonstrate the effectiveness of our strategy, we chose four different Tizen
