【中英雙語】512關(guān)于計算機專業(yè)移動安卓app錯誤崩盤報告維護的畢業(yè)設(shè)計論文英文英語外文文獻翻譯成品資料：自動發(fā)現(xiàn)、報告和修復(fù)android應(yīng)用程序崩潰（中英文雙語對照）

1、本文是中英對照畢業(yè)設(shè)計論文外文文獻翻譯，下載后無需調(diào)整復(fù)雜的格式直接可用！一輩子也就一次的事！文獻作者引用出處信息:Kevin Moran, Mario Linares-Va´squez, Carlos Bernal-Ca´rdenas, Christopher Vendome, and Denys Poshyvanyk, Automatically Discovering, Reporting an

2、d Reproducing Android Application Crashes,2018 IEEE International Conference on Software Testing, Verification and Validation (ICST) (如覺得年份太老，可改為近2 年，畢竟很多畢業(yè)生都這樣做)英文 5721 單詞，37485 字符(字符就是印刷符)，中文 9904 漢字。 （如果字數(shù)多了，可自行刪減，大

3、多數(shù)學(xué)校都是要求選取外文的一部分內(nèi)容進行翻譯的。 ）Automatically Discovering, Reporting and Reproducing Android Application CrashesAbstract—Mobile developers face unique challenges when detecting and reporting crashes in apps due to their prevai

4、ling GUI event-driven nature and additional sources of inputs (e.g., sensor readings). To support developers in these tasks, we introduce a novel, automated approach called CRASHSCOPE. This tool explores a given Android

5、app using systematic input generation, according to several strategies informed by static and dynamic analyses, with the intrinsic goal of triggering crashes. When a crash is detected, CRASHSCOPE generates an augmented c

6、rash report containing screenshots, detailed crash reproduction steps, the captured exception stack trace, and a fully replayable script that automatically reproduces the crash on a target device(s). We evaluated CRASHSC

7、OPE’s effectiveness in discovering crashes as compared to five state-of-the-art Android input generation tools on 61 applications. The results demonstrate that CRASHSCOPE performs about as well as current tools for detec

8、ting crashes and provides more detailed fault information. Additionally, in a study analyzing eight real-world Android app crashes, we found that CRASHSCOPE’s reports are easily readable and allow for reliable reproducti

9、on of crashes by presenting more explicit information than human written reports.I.INTRODUCTIONContinued growth in the mobile hardware and application marketplace is being driven by a landscape where users tend to prefer

10、 mobile smart devices and apps for tasks of contextual features, such as the orientation of the device, wireless interfaces, and sensors. We specifically tailored these features to test the common causes of app crashes a

11、s identified by previous studies [26], [45], [79]. During execution, CRASHSCOPE captures detailed information about the subject app, such as the inputs sent to the device, screenshots and GUI information, exceptions, and

12、 crash information. This information is then translated into detailed crash reports and replayable scripts, for any encountered crash.This paper makes the following noteworthy contributions:1) We design and implement a p

13、ractical and automatic approach for discovering, reporting, and reproducing Android application crashes, called CRASHSCOPE. To the best of the author’s knowledge, this is the first approach that is able to generate expre

14、ssive, detailed crash reports for mobile apps, including screenshots and augmented NL reproduction steps, in a completely automatic fashion. CRASHSCOPE is also one of the only available fully-automated Android testing ap

15、proaches that is practical from a developers’ perspective, requiring no instrumentation of the subject apps or OS. Our approach builds upon prior research in automated input generation for mobile apps, and implements sev

16、eral exploration strategies, informed by lightweight static analysis that are able to effectively detect crashes and exceptions;2) We perform a detailed evaluation of the crash detection abilities of CRASHSCOPE on 61 And

17、roid apps as compared to five state-of-the-art Android input generation tools: Dynodroid [53], Gui-Ripper [17], PUMA [36], A3E [20], and Monkey [7]. Our results show that CRASHSCOPE performs nearly as well as current too

18、ls in terms of detecting crashes, while automatically generating detailed reports and replayable scripts;3)We design and carry out a user study evaluating the reproducibility and readability of our automatically generate

19、d bug reports through comparison to human written crash reports for eight open source apps. The results indicate that CRASH- SCOPE reports offer more detail, while being at least as useful as the human written reports;4)