Open-source Android Apps Research Articles

An empirical study on compatibility issues in Android API field evolution

Context:The continuous evolution of the Android operating system requires regular API updates, which may affect the functionality of Android apps. This is becoming increasingly common due to the frequent evolution of the Android platform, which introduces new APIs and deprecates existing ones. Recent studies investigated API evolution to ensure the reliability of Android apps; however, they focused on API methods alone. Objectives:This study aims to understand how API fields evolve and how this affects API compatibility in real-world Android apps and their development. Method:We perform an empirical study on compatibility issues in Android API field evolution by analyzing the nature and resolution of these issues across 681 open-source Android apps. Results:Our experimental results yield interesting findings: (1) On average two API field compatibility issues exist per app in each tag; (2) Although API method evolution and API field evolution are related, current API method-level analysis techniques may fail to detect numerous API field compatibility issues; (3) Different types of checks are preferred when addressing different types of compatibility issues; (4) It takes on average three and a half months for an API field compatibility issue to get fixed since when it is introduced; (5) Developers pay proper attention to API field compatibility issues and address them soon after becoming aware of them in the apps. Conclusion:These findings highlight the significance of including API fields in future research on API evolution and can assist developers and researchers in understanding, detecting, and handling compatibility issues in API field evolution.

DinoDroid: Testing Android Apps Using Deep Q-Networks

The large demand of mobile devices creates significant concerns about the quality of mobile applications (apps). Developers need to guarantee the quality of mobile apps before it is released to the market. There have been many approaches using different strategies to test the GUI of mobile apps. However, they still need improvement due to their limited effectiveness. In this article, we propose DinoDroid, an approach based on deep Q-networks to automate testing of Android apps. DinoDroid learns a behavior model from a set of existing apps and the learned model can be used to explore and generate tests for new apps. DinoDroid is able to capture the fine-grained details of GUI events (e.g., the content of GUI widgets) and use them as features that are fed into deep neural network, which acts as the agent to guide app exploration. DinoDroid automatically adapts the learned model during the exploration without the need of any modeling strategies or pre-defined rules. We conduct experiments on 64 open-source Android apps. The results showed that DinoDroid outperforms existing Android testing tools in terms of code coverage and bug detection.

Characterizing and Finding System Setting-Related Defects in Android Apps

Android, the most popular mobile system, offers a number of user-configurable system settings (e.g., network, location, and permission) for controlling devices and apps. Even popular, well-tested apps may fail to properly adapt their behaviors to diverse setting changes, thus frustrating their users. However, there exists no effort to systematically investigate such defects. To this end, we conduct the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">first</i> large-scale empirical study to understand and characterize these <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">system setting-related defects</i> (in short as “setting defects”), which <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reside in apps and are triggered by system setting changes</i> . We devote substantial manual effort ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">over four person-months</i> ) to analyze 1,074 setting defects from 180 popular apps on GitHub. We investigate the impact, root causes, and consequences of these setting defects and their correlations. We find that (1) setting defects have a wide impact on apps' correctness with diverse root causes, (2) the majority of these defects ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\approx$</tex-math></inline-formula> 70.7%) cause non-crashing (logic) failures, and (3) some correlations exist between the setting categories, root causes, and consequences. Motivated and informed by these findings, we propose two bug-finding techniques that can synergistically detect setting defects from both the GUI and code levels. Specifically, at the GUI level, we design and introduce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">setting-wise metamorphic fuzzing</i> , the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">first</i> automated dynamic testing technique to detect setting defects (causing crash <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">and</i> non-crashing failures, respectively) for Android apps. We implement this technique as an end-to-end, automated GUI testing tool named <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SetDroid</small> . At the code level, we distill two major fault patterns and implement a static analysis tool named <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SetChecker</small> to identify potential setting defects. We evaluate <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SetDroid</small> and <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SetChecker</small> on 26 popular, open-source Android apps, and they find 48 unique, previously-unknown setting defects. To date, 35 have been confirmed and 21 have been fixed by app developers. We also apply <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SetDroid</small> and <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SetChecker</small> on five highly popular industrial apps, namely WeChat, QQMail, TikTok, CapCut, and AlipayHK, all of which each have billions of monthly active users. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SetDroid</small> successfully detects 17 previously unknown setting defects in these apps' latest releases, and all defects have been confirmed and fixed by the app vendors. After that, we collaborate with ByteDance and deploy these two bug-finding techniques internally to stress-test TikTok, one of its major app products. Within a two-month testing campaign, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SetDroid</small> successfully finds 53 setting defects, and <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SetChecker</small> finds 22 ones. So far, 59 have been confirmed and 31 have been fixed. All these defects escaped from prior developer testing. By now, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SetDroid</small> has been integrated into ByteDance's official app testing infrastructure named <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FastBot</small> for daily testing. These results demonstrate the strong effectiveness and practicality of our proposed techniques.

Understanding and Detecting Fragmentation-Induced Compatibility Issues for Android Apps

Android ecosystem is heavily fragmented. The numerous combinations of different device models and operating system versions make it impossible for Android app developers to exhaustively test their apps, and thus various compatibility issues arise. Unfortunately, little is known on the characteristics of such fragmentation-induced compatibility issues. No mature tools exist to help developers quickly diagnose and fix these issues. To bridge the gap, we conducted an empirical study on 220 real-world compatibility issues collected from five popular open-source Android apps. We further interviewed Android practitioners and conducted an online survey to gain insights from real practices. Via the studies, we characterized compatibility issues, investigated common practices to handle compatibility issues, and disclosed that these issues exhibit common patterns. With these findings, we propose a technique, FicFinder, to automatically detect compatibility issues in Android apps. FicFinder performs static code analysis based on a model that captures Android APIs as well as their associated context by which compatibility issues can be triggered. FicFinder reports actionable debugging information to developers when it detects potential issues. We evaluated FicFinder with 53 large-scale open-source Android apps. The results show that FicFinder can precisely detect compatibility issues in these apps and uncover previously-unknown issues.

Characterizing the evolution of statically-detectable performance issues of Android apps

Mobile apps are playing a major role in our everyday life, and they are tending to become more and more complex and resource demanding. Because of that, performance issues may occur, disrupting the user experience or, even worse, preventing an effective use of the app. Ultimately, such problems can cause bad reviews and influence the app success. Developers deal with performance issues thorough dynamic analysis, i.e., performance testing and profiler tools, albeit static analysis tools can be a valid, relatively inexpensive complement for the early detection of some such issues. This paper empirically investigates how potential performance issues identified by a popular static analysis tool — Android Lint — are actually resolved in 316 open source Android apps among 724 apps we analyzed. More specifically, the study traces the issues detected by Android Lint since their introduction until they resolved, with the aim of studying (i) the overall evolution of performance issues in apps, (ii) the proportion of issues being resolved, as well as (iii) the distribution of their survival time, and (iv) the extent to which issue resolution are documented by developers in commit messages. Results indicate how some issues, especially related to the lack of resource recycle, tend to be more frequent than others. Also, while some issues, primarily of algorithmic nature, tend to be resolved quickly through well-known patterns, others tend to stay in the app longer, or not to be resolved at all. Finally, we found how only 10% of the issue resolution is documented in commit messages.

A3: Assisting Android API Migrations Using Code Examples

The fast-paced evolution of Android APIs has posed a challenging task for Android app developers. To leverage Android's frequently released APIs, developers must often spend considerable effort on API migrations. Prior research and Android official documentation typically provide enough information to guide developers in identifying the API calls that must be migrated and the corresponding API calls in an updated version of Android (what to migrate). However, API migration remains a challenging task since developers lack the knowledge of how to migrate the API calls. There exist code examples, such as Google Samples, that illustrate the usage of APIs. We posit that by analyzing the changes of API usage in code examples, we can learn API migration patterns to assist developers with API Migrations. In this paper, we propose an approach that learns API migration patterns from code examples, applies these patterns to the source code of Android apps for API migration, and presents the results to users as potential migration solutions. To evaluate our approach, we migrate API calls in open source Android apps by learning API migration patterns from code examples. We find that our approach can successfully learn API migration patterns and provide API migration assistance in 71 out of 80 cases. Our approach can either migrate API calls with little to no extra modifications needed or provide guidance to assist with the migrations. Through a user study, we find that adopting our approach can reduce the time spent on migrating APIs, on average, by 29%. Moreover, our interviews with app developers highlight the benefits of our approach when seeking API migrations. Our approach demonstrates the value of leveraging the knowledge contained in software repositories to facilitate API migrations.

An empirical catalog of code smells for the presentation layer of Android apps

Software developers, including those of the Android mobile platform, constantly seek to improve their applications’ maintainability and evolvability. Code smells are commonly used for this purpose, as they indicate symptoms of design problems. However, although the literature presents a variety of code smells, such as God Class and Long Method, characteristics that are specific to the underlying technologies are not taken into account. The presentation layer of an Android app, for example, implements specific architectural decisions from the Android platform itself (such as the use of Activities, Fragments, and Listeners) as well as deal with and integrate different types of resources (such as layouts and images). Through a three-step study involving 246 Android developers, we investigated code smells that developers perceive for this part of Android apps. We devised 20 specific code smells and collected the developers’ perceptions of their frequency and importance. We also implemented a tool that identifies the proposed code smells and studied their prevalence in 619 open-source Android apps. Our findings suggest that: 1) developers perceive smells specific to the presentation layer of Android apps; 2) developers consider these smells to be of high importance and frequency; and 3) the proposed smells occur in real-world Android apps. Our domain-specific smells can be leveraged by developers, researchers, and tool developers for searching potentially problematic pieces of code.

Effective testing of Android apps using extended IFML models

The last decade has seen a vast proliferation of mobile apps. To improve the reliability of such apps, various techniques have been developed to automatically generate tests for them. While such techniques have been proven to be useful in producing test suites that achieve significant levels of code coverage, there is still enormous demand for techniques that effectively generate tests to exercise more code and detect more bugs of apps.We propose in this paper the Adamant approach to automated Android app testing. Adamant utilizes models that incorporate valuable human knowledge about the behaviours of the app under consideration to guide effective test generation, and the models are encoded in an extended version of the Interaction Flow Modeling Language (IFML).In an experimental evaluation on 10 open source Android apps, Adamant generated over 130 test actions per minute, achieved around 68% code coverage, and exposed 8 real bugs, significantly outperforming other test generation tools like Monkey, AndroidRipper, and Gator in terms of code covered and bugs detected.

PerHelper: Helping Developers Make Better Decisions on Permission Uses in Android Apps

Permission-related issues in Android apps have been widely studied in our research community, while most of the previous studies considered these issues from the perspective of app users. In this paper, we take a different angle to revisit the permission-related issues from the perspective of app developers. First, we perform an empirical study on investigating how we can help developers make better decisions on permission uses during app development. With detailed experimental results, we show that many permission-related issues can be identified and fixed during the application development phase. In order to help developers to identify and fix these issues, we develop PerHelper, an IDEplugin to automatically infer candidate permission sets, which help guide developers to set permissions more effectively and accurately. We integrate permission-related bug detection into PerHelper and demonstrate its applicability and flexibility through case studies on a set of open-source Android apps.

DroidLeaks: a comprehensive database of resource leaks in Android apps

Resource leaks in Android apps are pervasive. They can cause serious performance degradation and system crashes. In recent years, many resource leak detection techniques have been proposed to help Android developers correctly manage system resources. Yet, there exist no common databases of real-world bugs for effectively comparing such techniques to understand their strengths and limitations. This paper describes our effort towards constructing such a bug database named DroidLeaks. To extract real resource leak bugs, we mined 124,215 code revisions of 34 popular open-source Android apps. After automated filtering and manual validation, we successfully found 292 fixed resource leak bugs, which cover a diverse set of resource classes, from 32 analyzed apps. To understand these bugs, we conducted an empirical study, which revealed the characteristics of resource leaks in Android apps and common patterns of resource management mistakes made by developers. To further demonstrate the usefulness of our work, we evaluated eight resource leak detectors from both academia and industry on DroidLeaks and performed a detailed analysis of their performance. We release DroidLeaks for public access to support future research.

A Commit Messages-Based Bug Localization for Android Applications

Recently, there has been consistent growth in Android applications (apps). Under these circumstances, software maintenance for Android apps becomes an essential and important task. The core of software maintenance is to locate bugs in source files. Previous bug localization approaches mainly focus on open-source desktop software (e.g. Eclipse, Mozilla, GCC). Even though a few studies locate the bugs in the Android apps, they are dedicated to a special app named ZXing, without developing a general method to locate the bugs in Android apps by taking into account the unique characteristics of Android apps’ bug reports. Such characteristics include fewer number of historical bug reports, insufficient detailed description, etc. These characteristics hinder existing localization approaches from being directly delivered to Android apps, because lack of enough information degrades the performance of those localization approaches relying on historical bug reports. Commit messages include more informative data which can provide the details of reported bugs. Therefore, in this paper, we propose a novel information retrieval-based approach which utilizes commit messages to locate new bugs in Android apps. This approach not only considers the structured textual similarity between the given bug and the candidate source files, but also computes the unstructured textual similarities between the new bug and the commit messages linked to the corresponding source files. According to the experimental results on 10 popular open-source Android apps managed by GitHub, our approach outperforms the state-of-the-art bug localization methods that include BugLocator, BLUiR, and two-phase model.

Automatic, highly accurate app permission recommendation

To ensure security and privacy, Android employs a permission mechanism which requires developers to explicitly declare the permissions needed by their applications (apps). Users must grant those permissions before they install apps or during runtime. This mechanism protects users’ private data, but also imposes additional requirements on developers. For permission declaration, developers need knowledge about what permissions are necessary to implement various features of their apps, which is difficult to acquire due to the incompleteness of Android documentation. To address this problem, we present a novel permission recommendation system named PerRec for Android apps. PerRec leverages mining-based techniques and data fusion methods to recommend permissions for given apps according to their used APIs and API descriptions. The recommendation scores of potential permissions are calculated by a composition of two techniques which are implemented as two components of PerRec: a collaborative filtering component which measures similarities between apps based on semantic similarities between APIs; and a content-based recommendation component which automatically constructs profiles for potential permissions from existing apps. The two components are combined in PerRec for better performance. We have evaluated PerRec on 730 apps collected from Google Play and F-Droid, a repository of free and open source Android apps. Experimental results show that our approach significantly improves the state-of-the-art approaches $$APRec^{CF_{correlation}}$$ , $$APRec^{TEXT}$$ and Axplorer.

난독화된 오픈소스 안드로이드 앱을 탐지하는 기법 구현

최근 오픈소스 소프트웨어(Open-Source Software, OSS)가 널리 사용됨에 따라, OSS 라이선스 위반 사례가 많이 발생하고 있다. 한편 OpenSSL, WebView 등 대표적인 OSS에서 보안 취약점이 발견되고 있으며 이로 인한 보안 위협도 증대되고 있다. 이에 OSS를 탐지하기 위한 연구가 진행되고 있으나, 코드 난독화(code obfuscation)를 고려한 연구는 거의 수행되지 않았다. 본 논문에서는 난독화된 안드로이드 앱이 OSS로부터 생성되었는지를 식별하는 기법을 제안하고 실험을 통해 제안 기법을 검증한다. 제안 기법은 실행코드의 클래스 계층구조 정보(Class Hierarchy Information: CHI) 기반으로 오픈소스 앱을 탐지한다. 먼저, 난독화되지 않은 안드로이드 앱들을 대상으로 CHI 기반 특징정보들을 DB로 구축한 후, 난독화된 앱으로부터 CHI 기반 특징정보를 추출하여 DB의 특징정보와 비교하여 일치하면 OSS로 탐지한다. 두 번째 실험에서는, 난독화된 앱을 역난독화(de-obfuscation)하여 복원한 후, 역난독화된 앱으로부터 CHI 기반 특징정보를 추출하여 DB의 특징정보와 비교하여 오픈 소스 여부를 탐지한다. 실험 결과, CHI기반 특징정보가 제어흐름 난독화나 변수 명 난독화에는 강인하나 클래스 명 난독화에는 유효하지 않았다.

Studying the characteristics of logging practices in mobile apps: a case study on F-Droid

Logging is a common practice in software engineering. Prior research has investigated the characteristics of logging practices in system software (e.g., web servers or databases) as well as desktop applications. However, despite the popularity of mobile apps, little is known about their logging practices. In this paper, we sought to study logging practices in mobile apps. In particular, we conduct a case study on 1,444 open source Android apps in the F-Droid repository. Through a quantitative study, we find that although mobile app logging is less pervasive than server and desktop applications, logging is leveraged in almost all studied apps. However, we find that there exist considerable differences between the logging practices of mobile apps and the logging practices in server and desktop applications observed by prior studies. In order to further understand such differences, we conduct a firehouse email interview and a qualitative annotation on the rationale of using logs in mobile app development. By comparing the logging level of each logging statement with developers’ rationale of using the logs, we find that all too often (35.4%), the chosen logging level and the rationale are inconsistent. Such inconsistency may prevent the useful runtime information to be recorded or may generate unnecessary logs that may cause performance overhead. Finally, to understand the magnitude of such performance overhead, we conduct a performance evaluation between generating all the logs and not generating any logs in eight mobile apps. In general, we observe a statistically significant performance overhead based on various performance metrics (response time, CPU and battery consumption). In addition, we find that if the performance overhead of logging is significantly observed in an app, disabling the unnecessary logs indeed provides a statistically significant performance improvement. Our results show the need for a systematic guidance and automated tool support to assist in mobile logging practices.

Towards prioritizing user-related issue reports of mobile applications

The competitive market of mobile applications (apps) has driven app developers to pay more attention to addressing the issues of mobile apps. Prior studies have shown that addressing the issues that are reported in user-reviews shares a statistically significant relationship with star-ratings. However, despite the prevalence and importance of user-reviews and issue reports prioritization, no prior research has analyzed the relationship between issue reports prioritization and star-ratings. In this paper, we integrate user-reviews into the process of issue reports prioritization. We propose an approach to map issue reports that are recorded in issue tracking systems to user-reviews. Through an empirical study of 326 open-source Android apps, our approach achieves a precision of 79% in matching user-reviews with issue reports. Moreover, we observe that prioritizing the issue reports that are related to user-reviews shares a significant positive relationship with star-ratings. Furthermore, we use the top apps, in terms of star-ratings, to train a model for prioritizing issue reports. It is a good practice to learn from the top apps as there is no well-established approach for prioritizing issue reports. The results show that mobile apps with a similar prioritization approach to our trained model achieve higher star-ratings.

A Static Analysis of Android Source Code for Lifecycle Development Usage Patterns

Building robust Android apps is a non-trivial task that requires skilled developers to understand various Android platform peculiarities. However, among the Android developers community, a large fractions are considered to be novice and inexperienced developers. One of the main peculiarities in the Android app development is the activity lifecycle model. A developer needs to have deep understanding of the different lifecycle states and callback methods that an Android activity can go through during its runtime. These callback methods are called by the system whenever an app activity changes its state. The developer needs to override appropriate callback methods correctly to avoid app memory leaks and data loss or other phone resource compromise. Detailed static analysis of software applications provides actionable insights and helps us to understand how applications are actually built. Although there have been many studies focusing on static analysis of Android apps in the areas of testing, quality, design, privacy and security; no studies to date focus on lifecycle development practices and usage patterns thus far. In this paper, we analyzed 842 open-source Android apps containing 5577 activities to explore and understand how Android developers actually comply with best practices regarding the Android activity lifecycle model. We developed a tool named SAALC that is capable of analyzing Android activities and extracting valuable information about lifecycle callback methods usage. Our results show, which callback methods are implemented and the nature of the code they contain. The results also show incorrect implementation of the callback methods and incorrect acquiring and releasing of system resources in many Android apps and we argue that a relatively large fraction of Android developers do not sufficiently well understand the app lifecycle model. We also discuss our results in comparison to the Android app lifecycle model best practices.

Crowdsourcing user reviews to support the evolution of mobile apps

In recent software development and distribution scenarios, app stores are playing a major role, especially for mobile apps. On one hand, app stores allow continuous releases of app updates. On the other hand, they have become the premier point of interaction between app providers and users. After installing/updating apps, users can post reviews and provide ratings, expressing their level of satisfaction with apps, and possibly pointing out bugs or desired features. In this paper we empirically investigate—by performing a study on the evolution of 100 open source Android apps and by surveying 73 developers—to what extent app developers take user reviews into account, and whether addressing them contributes to apps’ success in terms of ratings. In order to perform the study, as well as to provide a monitoring mechanism for developers and project managers, we devised an approach, named CRISTAL, for tracing informative crowd reviews onto source code changes, and for monitoring the extent to which developers accommodate crowd requests and follow-up user reactions as reflected in their ratings. The results of our study indicate that (i) on average, half of the informative reviews are addressed, and over 75% of the interviewed developers claimed to take them into account often or very often, and that (ii) developers implementing user reviews are rewarded in terms of significantly increased user ratings.

On code reuse from StackOverflow: An exploratory study on Android apps

Context: Source code reuse has been widely accepted as a fundamental activity in software development. Recent studies showed that StackOverflow has emerged as one of the most popular resources for code reuse. Therefore, a plethora of work proposed ways to optimally ask questions, search for answers and find relevant code on StackOverflow. However, little work studies the impact of code reuse from StackOverflow.Objective: To better understand the impact of code reuse from StackOverflow, we perform an exploratory study focusing on code reuse from StackOverflow in the context of mobile apps. Specifically, we investigate how much, why, when, and who reuses code. Moreover, to understand the potential implications of code reuse, we examine the percentage of bugs in files that reuse StackOverflow code.Method: We perform our study on 22 open source Android apps. For each project, we mine their source code and use clone detection techniques to identify code that is reused from StackOverflow. We then apply different quantitative and qualitative methods to answer our research questions.Results: Our findings indicate that 1) the amount of reused StackOverflow code varies for different mobile apps, 2) feature additions and enhancements in apps are the main reasons for code reuse from StackOverflow, 3) mid-age and older apps reuse StackOverflow code mostly later on in their project lifetime and 4) that in smaller teams/apps, more experienced developers reuse code, whereas in larger teams/apps, the less experienced developers reuse code the most. Additionally, we found that the percentage of bugs is higher in files after reusing code from StackOverflow.Conclusion: Our results provide insights on the potential impact of code reuse from StackOverflow on mobile apps. Furthermore, these results can benefit the research community in developing new techniques and tools to facilitate and improve code reuse from StackOverflow.

An Empirical Analysis of Android Apps Bug and Automated Testing Approach for Android Apps

Android platforms and its applications (apps) have gained tremendous popularity recently, hence the reliability of Android apps is becoming increasingly important. Due to the novelty of the Android platform, apps are prone to errors, which would affect user experience and requires frequent bug fixes. In this paper, an empirical study on bugs in some widely-used open-source Android apps from diverse categories such as media, tools and communication were performed. Based on the findings of the empirical study, an approach for automating the testing process for detecting Android apps GUI bugs was presented. We show how the approach helped to re-discover existing bugs and find new bugs, and how it could be used to prevent certain bug categories. Our empirical study and automated testing approach have the potential to help developers increase the quality of Android apps.

MobiGUITAR: Automated Model-Based Testing of Mobile Apps

As mobile devices become increasingly smarter and more powerful, so too must the engineering of their software. User-interface-driven system testing of these devices is gaining popularity, with each vendor releasing some automation tool. However, these tools are inappropriate for amateur programmers, an increasing portion of app developers. MobiGUITAR (Mobile GUI Testing Framework) provides automated GUI-driven testing of Android apps. It's based on observation, extraction, and abstraction of GUI widgets' run-time state. The abstraction is a scalable state machine model that, together with test coverage criteria, provides a way to automatically generate test cases. When applied to four open-source Android apps, MobiGUITAR automatically generated and executed 7,711 test cases and reported 10 new bugs. Some bugs were Android-specific, stemming from the event- and activity-driven nature of Android.