國立虎尾科技大學 |

Automating Software Development for Mobile Computing Platforms.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Automating Software Development for Mobile Computing Platforms./
作者:	Moran, Kevin Patrick.
面頁冊數:	1 online resource (223 pages)
附註:	Source: Dissertation Abstracts International, Volume: 80-03(E), Section: B.
Contained By:	Dissertation Abstracts International80-03B(E).
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9780438575080

Automating Software Development for Mobile Computing Platforms.
Moran, Kevin Patrick.

Automating Software Development for Mobile Computing Platforms. - 1 online resource (223 pages)

Source: Dissertation Abstracts International, Volume: 80-03(E), Section: B.

Thesis (Ph.D.)--The College of William and Mary, 2018.

Includes bibliographical references

Mobile devices such as smartphones and tablets have become ubiquitous in today's computing landscape. These devices have ushered in entirely new populations of users, and mobile operating systems are now outpacing more traditional "desktop" systems in terms of market share. The applications that run on these mobile devices (often referred to as "apps") have become a primary means of computing for millions of users and, as such, have garnered immense developer interest. These apps allow for unique, personal software experiences through touch-based UIs and a complex assortment of sensors. However, designing and implementing high quality mobile apps can be a difficult process. This is primarily due to challenges unique to mobile development including change-prone APIs and platform fragmentation, just to name a few.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780438575080Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Automating Software Development for Mobile Computing Platforms.
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500 $a Source: Dissertation Abstracts International, Volume: 80-03(E), Section: B.
500 $a Adviser: Denys Poshyvanyk.
502 $a Thesis (Ph.D.)--The College of William and Mary, 2018.
504 $a Includes bibliographical references
520 $a Mobile devices such as smartphones and tablets have become ubiquitous in today's computing landscape. These devices have ushered in entirely new populations of users, and mobile operating systems are now outpacing more traditional "desktop" systems in terms of market share. The applications that run on these mobile devices (often referred to as "apps") have become a primary means of computing for millions of users and, as such, have garnered immense developer interest. These apps allow for unique, personal software experiences through touch-based UIs and a complex assortment of sensors. However, designing and implementing high quality mobile apps can be a difficult process. This is primarily due to challenges unique to mobile development including change-prone APIs and platform fragmentation, just to name a few.
520 $a In this dissertation we develop techniques that aid developers in overcoming these challenges by automating and improving current software design and testing practices for mobile apps. More specifically, we first introduce a technique, called Gvt, that improves the quality of graphical user interfaces (GUIs) for mobile apps by automatically detecting instances where a GUI was not implemented to its intended specifications. Gvt does this by constructing hierarchal models of mobile GUIs from metadata associated with both graphical mock-ups (i.e., created by designers using photo-editing software) and running instances of the GUI from the corresponding implementation. Second, we develop an approach that completely automates prototyping of GUIs for mobile apps. This approach, called ReDraw, is able to transform an image of a mobile app GUI into runnable code by detecting discrete GUI-components using computer vision techniques, classifying these components into proper functional categories (e.g., button, dropdown menu) using a Convolutional Neural Network (CNN), and assembling these components into realistic code. Finally, we design a novel approach for automated testing of mobile apps, called CrashScope, that explores a given Android app using systematic input generation with the intrinsic goal of triggering crashes. The GUI-based input generation engine is driven by a combination of static and dynamic analyses that create a model of an app's GUI and targets common, empirically derived root causes of crashes in Android apps.
520 $a We illustrate that the techniques presented in this dissertation represent significant advancements in mobile development processes through a series of empirical investigations, user studies, and industrial case studies that demonstrate the effectiveness of these approaches and the benefit they provide developers.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
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