App Testing Research Articles

Pengukuran Kesadaran Keamanan Informasi Dan Privasi Pada Pengguna Smartphone Android Di Indonesia

Based on statistical data, it is known that Android is the most popular smartphone with the largest number of users in the world, which is about 1.8 billion users. The high number of users also invite the many cases of information security and privacy caused by the lack of awareness of the user such as : spam, spoofing/phising, network incident, malware, uploading something personal data such as photos, phone numbers, addresses or having no antivirus. This study aims to find out about the awareness of the security of information and privacy of Android smartphone users by doing measurement of problem. The awareness has some dimensions such as attitude, knowledge and behavior with the seven focus areas of information security namely trust in app repository, misconception about app testing, security and agreement message, pirated application, adoption Security control, spam sms and report of security incidents and three focus areas of privacy are perceived surveillance, perceived intrusion, secondary use of information. This research uses analytical hierarchy process (AHP) to measure the level of awareness of information security and privacy of smartphone users. Overall, the results of the research show that information security has an average level of awareness (71%) but the focus area of report for security incidents has a poor level of awareness (37%) this occur because users prefer to solve their own information security issues experienced and privacy has an average level of awareness (76%). However, for secondary use of information in attitude dimension has low awareness level (66%). Based on the results of this study, it can be concluded that smartphone users in Indonesia have a poor awareness level in maintaining security and privacy of their information.

Digital health app development standards: a systematic review protocol

IntroductionThere is currently a lack of clear and accepted standards for the development (planning, requirement analysis and research, design and application testing) of apps for medical and healthcare use which...

Considerations for the Development of Mobile Phone Apps to Support Diabetes Self-Management: Systematic Review.

BackgroundThere is increased research interest in the use of mobile phone apps to support diabetes management. However, there are divergent views on what constitute the minimum standards for inclusion in the development of mobile phone apps. Mobile phone apps require an evidence-based approach to development which will consequently impact on their effectiveness. Therefore, comprehensive information on developmental considerations could help designers and researchers to develop innovative and effective patient-centered self-management mobile phone apps for diabetes patients.ObjectiveThis systematic review examined the developmental considerations adopted in trials that engaged mobile phone applications for diabetes self-management.MethodsA comprehensive search strategy was implemented across 5 electronic databases; Medline, Scopus, Social Science Citation Index, the Cochrane Central Register of Controlled Trials and Cumulative Index of Nursing and Allied Health Literature (CINALHL) and supplemented by reference list from identified studies. Study quality was evaluated using the Joanna Briggs Critical appraisal checklist for trials. Information on developmental factors (health behavioral theory, functionality, pilot testing, user and clinical expert involvements, data privacy and app security) were assessed across experimental studies using a template developed for the review.ResultsA total of 11 studies (10 randomized controlled trials and 1 quasi-experimental trial) that fitted the inclusion criteria were identified. All the included studies had the functionality of self-monitoring of blood glucose. However, only some of them included functions for data analytics (7/11, 63.6%), education (6/11, 54.5%) and reminder (6/11, 54.5%). There were 5/11(45.5%) studies with significantly improved glycosylated hemoglobin in the intervention groups where educational functionality was present in the apps used in the 5 trials. Only 1 (1/11, 9.1%) study considered health behavioral theory and user involvement, while 2 (2/11, 18.1%) other studies reported the involvement of clinical experts in the development of their apps. There were 4 (4/11, 36.4%) studies which referred to data security and privacy considerations during their app development while 7 (7/12, 63.6%) studies provided information on pilot testing of apps before use in the full trial. Overall, none of the studies provided information on all developmental factors assessed in the review.ConclusionsThere is a lack of elaborate and detailed information in the literature regarding the factors considered in the development of apps used as interventions for diabetes self-management. Documentation and inclusion of such vital information will foster a transparent and shared decision-making process that will ultimately lead to the development of practical and user-friendly self-management apps that can enhance the quality of life for diabetes patients.

Characterizing mobile apps from a source and test code viewpoint

Context: while the mobile computing market has expanded and become critical, the amount and complexity of mobile apps have also increased. To assure reliability, these apps require software engineering methods, mainly verification, validation, and testing. However, mobile app testing is a challenging activity due to the diversity and limitations found in mobile devices. Thus, it would be interesting to characterize mobile apps in hopes of assisting in the definition of more efficient and effective testing approaches. Objective: this paper aims to identify and quantify the specific characteristics of mobile apps so that testers can draw from this knowledge and tailor software testing activities to mobile apps. We investigate the presence of automated tests, adopted frameworks, external connectivity, graphical user interface (GUI) elements, sensors, and different system configurations. Method: we developed a tool to support the automatic extraction of characteristics from Android apps. We conducted an empirical study with a sample of 663 open source mobile apps. Results: we found that one third of the projects perform automated testing. The frameworks used in these projects can be divided into three groups: unit testing, GUI testing, and mocking. There is a medium correlation between project size and test presence. Specific features of mobile apps (connectivity, GUI, sensors, and multiple configurations) are present in the projects, however, they are fully covered by tests. Conclusion: automated tests are still not developed in a systematic way. Interestingly, measures of app popularity (number of downloads and rating) do not seem to be correlated with the presence of tests. However, the results show a correlation of the project size and more critical domains with the existence of automated tests. Although challenges such as connectivity, sensors, and multiple configurations are present in the examined apps, only one tool has been identified to support the testing of these challenges.

Mobile App Testing: Challenges, Strategy and Approaches

Mobile App Testing: Challenges, Strategy and Approaches

PENGEMBANGAN LAYANAN PENYEBARAN INFORMASI AKADEMIK BERBASIS ANDROID

E-pengumuman is a multimedia-based information dissemination application that utilizes internet network as data eye media. The use of android mobile apps as a tool for accessing anything connected to the internet helps the customers with information submitted by the app site. Make the Internet as a central storage server to manage data as well as information. Services available on the application can be accessed through student information websites, faculty, and courses. While the news can be accessed via mobile Android. The results of this app testing show that this application can be used as a delivery service and receive information appropriately.

Virtual Laboratory Application Development for Mobile Terminal

It is highly important to finish the experiments while learning the course of digital circuit design is to confirm the theoretical knowledge in the experiment, which makes the learning process an extremely good expansion. However, in the actual situation, digital design laboratories are often in short supply, which is mainly reflected in its spatial limitations, the limitations on the number of instruments and instrument stability limitations, especially in some poor and developing countries. This paper designed a mobile virtual laboratory application (VL-APP) for learning digital design to help students perform virtual experiments to conduct new thoughts anywhere and anytime, which also support anyone familiar with the instruments used in the digital area. It mainly aims at the simulation experiment of a specific experiment in the digital design experiment, and provides the model and script basis for the users to design the experiment independently. Selecting Unity3D and 3ds-max for the software platform, Android for the test installation environment, the APP testing results showed that it is good enough to be applied right now.

A MACHINE LEARNING BASED APPROACH FOR DETECTING NON-DETERMINISTIC TESTS AND ITS ANALYSIS IN MOBILE APPLICATION TESTING

Hundreds of different mobile devices are on the market, produced by different vendors, and with different software features and hardware components. Mobile applications, while running on different devices, may behave differently due to variations in the hardware or O.S. components. Since mobile applications are expected to be deployed and executed on diverse mobile platforms, they must be validated on different mobile platforms and devices. Due to the peculiarities of mobile application development, there is a need for a quality assurance approach that focuses on its challenges. Moreover, mobile test executions take a long time because all the tests were executed in different environments and developers had to create complex tear down procedures. Such procedures were lengthy and far from perfect, leading to unpredictable failures. Regression testing is a crucial part of Mobile app development and it checks that software changes do not break existing functionality. An important assumption of regression testing is that test outcomes are deterministic and tests are expected to either always pass or always fail for the same code under test. But unfortunately, in real projects multiple release cycles, some tests— often called flaky tests—have non-deterministic outcomes. These tests undermine the regression testing cycle as they make it difficult to rely on test results. These results significantly reduced the trust in the tests and thus undermined the whole mobile app test automation effort. We trained machine learning classifiers separately on each test result dataset and compared performance across datasets. The proposed model predicts result types as Non-Deterministic or Deterministic tests from the regression suite results executed in various release cycles.

Fuzzy Clustering of Crowdsourced Test Reports for Apps

DevOps is a new approach to drive a seamless Application (App) cycle from development to delivery. As a critical part to promote the successful implementation of DevOps, testing can significantly improve team productivity and reliably deliver user experience. However, it is difficult to use traditional testing to cover diverse mobile phones, network environments, operating systems, and so on. Hence, many large companies crowdsource their App testing tasks to workers from open platforms. In crowdsourced testing, test reports submitted by workers may be highly redundant, and their quality may vary sharply. Meanwhile, multi-bug test reports may be submitted, and their root causes are hard to diagnose. Hence, it is a time-consuming and tedious task for developers to manually inspect these test reports. To help developers address the above challenges, we issue the new problem of Fuzzy Clustering Test Reports (FULTER). Aiming to resolve FULTER, a series of barriers need to be overcome. In this study, we propose a new framework named Test Report Fuzzy Clustering Framework (TERFUR) by aggregating redundant and multi-bug test reports into clusters to reduce the number of inspected test reports. First, we construct a filter to remove invalid test reports to break through the invalid barrier . Then, a preprocessor is built to enhance the descriptions of short test reports to break through the uneven barrier . Last, a two-phase merging algorithm is proposed to partition redundant and multi-bug test reports into clusters that can break through the multi-bug barrier . Experimental results over 1,728 test reports from five industrial Apps show that TERFUR can cluster test reports by up to 78.15% in terms of AverageP , 78.41% in terms of AverageR , and 75.82% in terms of AverageF1 and outperform comparative methods by up to 31.69%, 33.06%, and 24.55%, respectively. In addition, the effectiveness of TERFUR is validated in prioritizing test reports for manual inspection.

Comparing men who have sex with men and transgender women who use Grindr, other similar social and sexual networking apps, or no social and sexual networking apps: Implications for recruitment and health promotion.

Researchers and public health professionals have increased their attention to GPS-based social and sexual networking applications (apps) tailored to gay, bisexual, other men who have sex with men (MSM) and transgender women. These populations continue to be disproportionately affected by HIV in the United States, therefore these apps, in particular Grindr, have become an important sampling venue for the recruitment of HIV-related research participants. As such, it is essential to identify differences among app users to avoid potential sampling bias. This paper seeks to identify differences in MSM and transgender women who use Grindr and those who use other similar apps. A community-based participatory research (CBPR) approach was used to recruit participants online who then completed a 25-item anonymous survey. Five domains were assessed: sociodemographics, HIV testing, sexual risk, substance abuse, and use of GPS-based social and sexual networking apps. 457 participants completed surveys. There were significant differences in the sociodemographic characteristics by app use, including age, race/ethnicity, sexual orientation, and outness. After adjusting for the sociodemographic characteristics associated with app use, there were significant differences in HIV risk and substance use between the groups. This paper is the first to report on findings that compare MSM and transgender women who report using Grindr to MSM and transgender women who report using other similar apps. GPS-based social and sexual networking apps may offer a valuable recruitment tool for future HIV research seeking to recruit populations at increased risk for HIV or those living with HIV for therapeutic trials. Because of the differences identified across users of different apps, these findings suggest that if researchers recruited participants from just one app, they could end up with a sample quite different than if they had recruited MSM and transgender women from other apps.

Client-Focused Security Assessment of mHealth Apps and Recommended Practices to Prevent or Mitigate Transport Security Issues.

BackgroundMobile health (mHealth) apps show a growing importance for patients and health care professionals. Apps in this category are diverse. Some display important information (ie, drug interactions), whereas others help patients to keep track of their health. However, insufficient transport security can lead to confidentiality issues for patients and medical professionals, as well as safety issues regarding data integrity. mHealth apps should therefore deploy intensified vigilance to protect their data and integrity. This paper analyzes the state of security in mHealth apps.ObjectiveThe objectives of this study were as follows: (1) identification of relevant transport issues in mHealth apps, (2) development of a platform for test purposes, and (3) recommendation of practices to mitigate them.MethodsSecurity characteristics relevant to the transport security of mHealth apps were assessed, presented, and discussed. These characteristics were used in the development of a prototypical platform facilitating streamlined tests of apps. For the tests, six lists of the 10 most downloaded free apps from three countries and two stores were selected. As some apps were part of these top 10 lists in more than one country, 53 unique apps were tested.ResultsOut of the 53 apps tested from three European App Stores for Android and iOS, 21/53 (40%) showed critical results. All 21 apps failed to guarantee the integrity of data displayed. A total of 18 apps leaked private data or were observable in a way that compromised confidentiality between apps and their servers; 17 apps used unprotected connections; and two apps failed to validate certificates correctly. None of the apps tested utilized certificate pinning. Many apps employed analytics or ad providers, undermining user privacy.ConclusionsThe tests show that many mHealth apps do not apply sufficient transport security measures. The most common security issue was the use of any kind of unprotected connection. Some apps used secure connections only for selected tasks, leaving all other traffic vulnerable.

Building a Model-Based GUI Test Automation System for Mobile Applications

With the rapid advance of mobile computing technology and wireless networking, there is a significant increase of mobile applications (apps). This brings new business requirements and demands in mobile software testing, and causes new issues and challenges in mobile test automation. Existing mobile application testing approaches mostly concentrate on GUI-based testing, load and performance testing without considering large-scale concurrent mobile app test automation, and model-based test coverage analysis. In this paper, a mobile hierarchical GUI model is proposed to present mobile operation scenario flows and gesture features in a hierarchical manner, in order to facilitate test dependency analysis in test automation. Mobile app test coverage analysis is performed based on GUI ripping models. The paper also presents a developed system that provides a test automation solution using GUI models. Finally, the paper reports a case study to indicate the feasibility and effectiveness of the proposed approach.

Music Streaming Services as Adjunct Therapies for Depression, Anxiety, and Bipolar Symptoms: Convergence of Digital Technologies, Mobile Apps, Emotions, and Global Mental Health

Mobile technologies and music are recognized as opportunities to address mental health challenges (1–3), while clinical and economic benefits of mobile health (mHealth) are currently studied (4–6). Herein, we describe feasibility of repurposing music streaming services as therapies for affective disorders. According to the World Health Organization, there are 350 million people worldwide suffering from depression, and 60 million people living with bipolar disorder. Patients with affective disorders such as depression, anxiety, or bipolar spectrum, and their caregivers are challenged with managing disease symptoms, long-term treatments, and disabilities. Between 1990 and 2010, there has been a 41% increase in public health burden of mental, neurological, and substance use disorders, as measured by disability-adjusted life years (7). Depression accounts for 40.5% of total disability-adjusted life years among mental and substance-use disorders, whereas anxiety and bipolar disorder account for 14.6 and 7%, respectively (8). A long-term morbidity in bipolar spectrum disorders emphasizes the needs to improve treatments for depression (9). Treatments of affective disorders include mainly antidepressant, antipsychotic medications, and cognitive behavioral therapy (CBT). The efficacy of antidepressants for children and adolescent patients (10), medication adherence, and limited access to CBT in many countries continue to be a challenge for public health. Treatments of depression, anxiety, and bipolar symptoms comprise CBT, psychosocial, and self-care interventions, also delivered via online and digital technologies (7, 11, 12). Opportunities for developing mobile apps and web-based interventions for neurological and mental disorders are economically feasible and coincide with the global adoption rates for smartphones (6, 13–16). Promising findings from clinical testing of mobile apps, e.g., in depression (17), are accompanied by challenges in patient engagement (18) and alignment of clinical and digital contents (19). Converting a mobile phone into low-cost virtual reality devices (exemplified by a Google VR cardboard) extends its potential medical applications (20, 21). Growing number of health-related wearables and devices measuring electrodermal activity (EDA), heart rate variability, or mobile electroencephalogram (EEG) systems, expand the use of digital technologies in medicine, including mental health [a 5-week treatment with EEG-based musical neurofeedback improved depression scores by 17% (22)]. Companies like Apple, Samsung, LG, Microsoft, Fitbit, Empatica, Emotiv, NeuroSky, or Muse develop smart watches and mobile EEG systems with health/wellness applications, whereas WellDoc, Akili Interactive, or Pear Therapeutics are engaged in converting mobile apps and games into medical device-based therapies for specific chronic diseases. Given accessibility of smartphones and the internet, we discuss opportunities for music streaming services to be developed as adjunct therapies and prevention of depressive, anxiety, and bipolar spectrum symptoms.

SIT: Sampling-based interactive testing for self-adaptive apps

Self-adaptive applications (“apps” for short) are useful but error-prone. This stems from developers’ inadequate consideration of environmental dynamics and uncertainty. Two features of self-adaptive apps, infinite reaction loop and uncertain interaction, bring additional challenges to software testing and make existing approaches ineffective. In this article, we propose a novel approach SIT (Sample-based Interactive Testing) to testing self-adaptive apps effectively and in a light-weight way. Our key insight is that a self-adaptive app’s input space can be systematically split, adaptively explored, and mapped to the testing of the app’s different behavior. This is achieved by our approach’s two components, an interactive app model and a test generation technique. The former captures characteristics of interactions between an app and its environment, and the latter uses adaptive sampling to explore an app’s input space and test its behavior. We experimentally evaluated our approach with real-world self-adaptive apps. The experimental results reported that our SIT improved the bug detection by 22.4–42.2%, but with a smaller time cost. Besides, SIT is also scalable with our tailored optimization techniques.

Mobile Phone Apps for Preventing Cancer Through Educational and Behavioral Interventions: State of the Art and Remaining Challenges.

BackgroundRapid developments in technology have encouraged the use of mobile phones in smoking cessation, promoting healthy diet, nutrition, and physical activity, sun safety, and cancer screening. Although many apps relating to the prevention of cancer and other chronic diseases are available from major mobile phone platforms, relatively few have been tested in research studies to determine their efficacy.ObjectiveIn this paper, we discuss issues related to the development and testing of new apps for preventing cancer through smoking cessation, sun safety, and other healthy behaviors, including key methodologic issues and outstanding challenges.MethodsAn exploratory literature review was conducted using bibliographic searches in PubMed and CINAHL with relevant search terms (eg, smartphones, smoking cessation, cancer prevention, cancer screening, and carcinogens) to identify papers published in English through October 2015.ResultsOnly 4 randomized controlled trials of the use of mobile phone apps for smoking cessation and 2 trials of apps for sun safety were identified, indicating that it is premature to conduct a systematic search and meta-analysis of the published literature on this topic.ConclusionsFuture studies should utilize randomized controlled trial research designs, larger sample sizes, and longer study periods to better establish the cancer prevention and control capabilities of mobile phone apps. In developing new and refined apps for cancer prevention and control, both health literacy and eHealth literacy should be taken into account. There is a need for culturally appropriate, tailored health messages to increase knowledge and awareness of health behaviors such as smoking cessation, cancer screening, and sun safety. Mobile phone apps are likely to be a useful and low-cost intervention for preventing cancer through behavioral changes.

Smartphone Applications to Support Tuberculosis Prevention and Treatment: Review and Evaluation.

BackgroundTuberculosis (TB) remains a major global health problem and is the leading killer due to a single infectious disease. Mobile health (mHealth)–based tools such as smartphone apps have been suggested as tools to support TB control efforts (eg, identification, contact tracing, case management including patient support).ObjectiveThe purpose of this review was to identify and assess the functionalities of mobile apps focused on prevention and treatment of TB.MethodsWe searched 3 online mobile app stores. Apps were included if they were focused on TB and were in English, Spanish, or Portuguese. For each included app, 11 functionalities were assessed (eg, inform, instruct, record), and searches were conducted to identify peer-review publications of rigorous testing of the available apps.ResultsA total of 1332 potentially relevant apps were identified, with 24 meeting our inclusion criteria. All of the apps were free to download, but 7 required login and password and were developed for specific clinics, regional sites, or research studies. Targeted users were mainly clinicians (n=17); few (n=4) apps were patient focused. Most apps (n=17) had 4 or fewer functions out of 11 (range 1-6). The most common functionalities were inform and record (n=15). Although a number of apps were identified with various functionalities to support TB efforts, some had issues such as incorrect spelling and grammar, inconsistent responses to data entry, problems with crashing, or links to features that had no data. Of more concern, some apps provided potentially harmful information to patients, such as links to natural remedies for TB and natural healers. One-third of the apps (8/24) had not been updated for more than a year and may no longer be supported. Peer-reviewed publications were identified for only two of the included apps. In the gray literature (not found in the app stores), three TB-related apps were identified as in progress, being launched, or tested.ConclusionsApps identified for TB prevention and treatment had minimal functionality, primarily targeted frontline health care workers, and focused on TB information (eg, general information, guidelines, and news) or data collection (eg, replace paper-based notification or tracking). Few apps were developed for use by patients and none were developed to support TB patient involvement and management in their care (eg, follow-up alerts/reminders, side effects monitoring) or improve interaction with their health care providers, limiting the potential of these apps to facilitate patient-centered care. Our evaluation shows that more refined work is needed to be done in the area of apps to support patients with active TB. Involving TB patients in treatment in the design of these apps is recommended.

A Systematic Review of Cognitive Behavioral Therapy and Behavioral Activation Apps for Depression.

Depression is a common mental health condition for which many mobile apps aim to provide support. This review aims to identify self-help apps available exclusively for people with depression and evaluate those that offer cognitive behavioural therapy (CBT) or behavioural activation (BA). One hundred and seventeen apps have been identified after searching both the scientific literature and the commercial market. 10.26% (n = 12) of these apps identified through our search offer support that seems to be consistent with evidence-based principles of CBT or BA. Taking into account the non existence of effectiveness/efficacy studies, and the low level of adherence to the core ingredients of the CBT/BA models, the utility of these CBT/BA apps are questionable. The usability of reviewed apps is highly variable and they rarely are accompanied by explicit privacy or safety policies. Despite the growing public demand, there is a concerning lack of appropiate CBT or BA apps, especially from a clinical and legal point of view. The application of superior scientific, technological, and legal knowledge is needed to improve the development, testing, and accessibility of apps for people with depression.

Mutation operators for testing Android apps

Context: Due to the widespread use of Android devices, Android applications (apps) have more releases, purchases, and downloads than apps for any other mobile devices. The sheer volume of code in these apps creates significant concerns about the quality of the software. However, testing Android apps is different from testing traditional Java programs due to the unique program structure and new features of apps. Simple testing coverage criteria such as statement coverage are insufficient to assure high quality of Android apps. While researchers show significant interest in finding better Android testing approaches, there is still a lack of effective and usable techniques to evaluate their proposed test selection strategies, and to ensure a reasonable number of effective tests.Objective: As mutation analysis has been found to be an effective way to design tests in other software domains, we hypothesize that it is also a viable solution for Android apps.Method: This paper proposes an innovative mutation analysis approach that is specific for Android apps. We define mutation operators specific to the characteristics of Android apps, such as the extensive use of XML files to specify layout and behavior, the inherent event-driven nature, and the unique Activity lifecycle structure. We also report on an empirical study to evaluate these mutation operators.Results: We have built a tool that uses the novel Android mutation operators to mutate the source code of Android apps, then generates mutants that can be installed and run on Android devices. We evaluated the effectiveness of Android mutation testing through an empirical study on real-world apps. This paper introduces several novel mutation operators based on a fault study of Android apps, presents a significant empirical study with real-world apps, and provides conclusions based on an analysis of the results.Conclusion: The results show that the novel Android mutation operators provide comprehensive testing for Android apps. Additionally, as applying mutation testing to Android apps is still at a preliminary stage, we identify challenges, possibilities, and future research directions to make mutation analysis for mobile apps more effective and efficient.

Automated Bidirectional Languages Localization Testing for Android Apps with Rich GUI

Mobile apps are everywhere. The release of apps on a worldwide scale requires them to be made available in many languages, including bidirectional languages. Developers and translators are usually different persons. While automatic testing by itself is important in general in order to be able to develop high quality software, such automatic tests become absolutely essential when developers that do not possess enough knowledge about right-to-left languages need to maintain code that is written for bidirectional languages. A few bidirectional localization tests of mobile applications exist. However, their functionality is limited since they only cover translations and adoption of locales. In this paper we present our approach for automating the bidirectional localization testing for Android applications with a complete consideration for BiDi-languages issues. The objective is to check for any localization defects in the product. The proposed methods are used to test issues of bidirectional apps in general and specifically for the Arabic language. The results show that the methods are able to effectively reveal deficiencies in the app’s design, ensure that the localized app matches all expectations of local users, and guarantee that the product is culturally congruent to local conventions.

Mobile Software Testing: Thoughts, Strategies, Challenges, and Experimental Study

Mobile devices have become more pervasive in our daily lives, and are gradually replacing regular computers to perform traditional processes like Internet browsing, editing photos, playing videos and sound track, and reading different files. The importance of mobile devices in our life necessitates more concerns of the reliability and compatibility of mobile applications, and thus, testing these applications arises as an important phase in mobile devices adaption process. This paper addressed various research directions on mobile applications testing by investigating essential concepts, scope, features and requirements for testing mobile application. We highlight the similarities and the differences between mobile APP testing and mobile web testing. Furthermore, we discuss and compare different mobile testing approaches and environments, and provide the challenges as emergent needs in test environments. As a case study, we compared the testing experience of hybrid application in an emulator and a real world device. The purpose of the experiment is to verify to which extent a virtual device can emulate a complete client experience. Set of experiments are conducted where five android mobile browsers are tested. Each browser will be on a real device as well as an emulated device with the same features (CPU used, memory size, etc). The application will be tested on the following metrics: Performance and function/behavior testing