Doctoral thesis (Dissertations and theses)
Taming Android App Crashes
Kong, Pingfan


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Keywords :
Android; Crash; Static Analysis; Program Repair; Testing
Abstract :
[en] App crashes constitute an important deterrence for app adoption in the android ecosystem. Yet, Android app developers are challenged by the limitation of test automation tools to ensure that released apps are free from crashes. In recent years, researchers have proposed various automation approaches in the literature. Unfortunately, the practical value of these approaches have not yet been confirmed by practitioner adoption. Furthermore, existing approaches target a variety of test needs which are relevant to different sets of problems, without being specific to app crashes. Resolving app crashes implies a chain of actions starting with their reproduction, followed by the associated fault localization, before any repair can be attempted. Each action however, is challenged by the specificity of Android. In particular, some specific mechanisms (e.g., callback methods, multiple entry points, etc.) of Android apps require Android-tailored crash-inducing bug locators. Therefore, to tame Android app crashes, practitioners are in need of automation tools that are adapted to the challenges that they pose. In this respect, a number of building blocks must be designed to deliver a comprehensive toolbox. First, the community lacks well-defined, large-scale datasets of real-world app crashes that are reproducible to enable the inference of valuable insights, and facilitate experimental validations of literature approaches. Second, although bug localization from crash information is relatively mature in the realm of Java, state-of-the-art techniques are generally ineffective for Android apps due to the specificity of the Android system. Third, given the recurrence of crashes and the substantial burden that they incur for practitioners to resolve them, there is a need for methods and techniques to accelerate fixing, for example, towards implementing Automated Program Repair (APR). Finally, the above chain of actions is for curative purposes. Indeed, this "reproduction, localization, and repair" chain aims at correcting bugs in released apps. Preventive approaches, i.e., approaches that help developers to reduce the likelihood of releasing crashing apps, are still absent. In the Android ecosystem, developers are challenged by the lack of detailed documentation about the complex Android framework API they use to develop their apps. For example, developers need support for precisely identifying which exceptions may be triggered by APIs. Such support can further alleviate the challenge related to the fact that the condition under which APIs are triggered are often not documented. In this context, the present dissertation aims to tame Android crashes by contributing to the following four building blocks: Systematic Literature Review on automated app testing approaches: We aim at providing a clear overview of the state-of-the-art works around the topic of Android app testing, in an attempt to highlight the main trends, pinpoint the main methodologies applied and enumerate the challenges faced by the Android testing approaches as well as the directions where the community effort is still needed. To this end, we conduct a Systematic Literature Review (SLR) during which we eventually identified 103 relevant research papers published in leading conferences and journals until 2016. Our thorough examination of the relevant literature has led to several findings and highlighted the challenges that Android testing researchers should strive to address in the future. After that, we further propose a few concrete research directions where testing approaches are needed to solve recurrent issues in app updates, continuous increases of app sizes, as well as the Android ecosystem fragmentation. Locating Android app crash-inducing bugs: We perform an empirical study on 500 framework-specific crashes from an open benchmark. This study reveals that 37 percent of the crash types are related to bugs that are outside the crash stack traces. Moreover, Android programs are a mixture of code and extra-code artifacts such as the Manifest file. The fact that any artifact can lead to failures in the app execution creates the need to position the localization target beyond the code realm. We propose ANCHOR, a two-phase suspicious bug location suggestion tool. ANCHOR specializes in finding crash-inducing bugs outside the stack trace. ANCHOR is lightweight and source code independent since it only requires the crash message and the apk file to locate the fault. Experimental results, collected via cross-validation and in-the-wild dataset evaluation, show that ANCHOR is effective in locating Android framework-specific crashing faults. Mining Android app crash fix templates: We propose a scalable approach, CraftDroid, to mine crash fixes by leveraging a set of 28 thousand carefully reconstructed app lineages from app markets, without the need for the app source code or issue reports. We develop a replicative testing approach that locates fixes among app versions which output different runtime logs with the exact same test inputs. Overall, we have mined 104 relevant crash fixes, further abstracted 17 fine-grained fix templates that are demonstrated to be effective for patching crashed apks. Finally, we release ReCBench, a benchmark consisting of 200 crashed apks and the crash replication scripts, which the community can explore for evaluating generated crash-inducing bug patches. Documenting framework APIs' unchecked exceptions: We propose Afuera, an automated tool that profiles Android framework APIs and provides information on when they can potentially trigger unchecked exceptions. Afuera relies on a static-analysis approach and a dedicated algorithm to examine the entire Android framework. With Afuera, we confirmed that 26739 unique unchecked exception instances may be triggered by invoking 5467 (24%) Android framework APIs. Afuera further analyzes the Android framework to inform about which parameter(s) of an API method can potentially be the cause of the triggering of an unchecked exception. To that end, Afuera relies on fully automated instrumentation and taint analysis techniques. Afuera is run to analyze 50 randomly sampled APIs to demonstrate its effectiveness.Evaluation results suggest that Afuera has perfect true positive rate. However, Afuera is affected by false negatives due to the limitation of state-of-the-art taint analysis techniques.
Disciplines :
Computer science
Author, co-author :
Kong, Pingfan ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX
Language :
Title :
Taming Android App Crashes
Defense date :
01 April 2021
Number of pages :
Institution :
Unilu - University of Luxembourg, Luxembourg City, Luxembourg
Degree :
Docteur en Informatique
Promotor :
Jury member :
Leonardo, Mariani
Ting, Su
Focus Area :
Security, Reliability and Trust
FnR Project :
FNR11620657 - Android Malicious Code Localisation: Catch Me If You Can!, 2017 (15/04/2017-14/04/2021) - Pingfan Kong
Funders :
FNR - Fonds National de la Recherche [LU]
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since 08 April 2021


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