6,752 research outputs found
A Domain Oriented LDA Model for Mining Product Defects from Online Customer Reviews
Online reviews provide important demand-side knowledge for product manufacturers to improve product quality. However, discovering and quantifying potential products’ defects from large amounts of online reviews is a nontrivial task. In this paper, we propose a Latent Product Defect Mining model that identifies critical product defects. We define domain-oriented key attributes, such as components and keywords used to describe a defect, and build a novel LDA model to identify and acquire integral information about product defects. We conduct comprehensive evaluations including quantitative and qualitative evaluations to ensure the quality of discovered information. Experimental results show that the proposed model outperforms the standard LDA model, and could find more valuable information. Our research contributes to the extant product quality analytics literature and has significant managerial implications for researchers, policy makers, customers, and practitioners
Automatically Discovering, Reporting and Reproducing Android Application Crashes
Mobile developers face unique challenges when detecting and reporting crashes
in apps due to their prevailing 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 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
crash 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
CRASHSCOPE'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
detecting 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 reproduction of
crashes by presenting more explicit information than human written reports.Comment: 12 pages, in Proceedings of 9th IEEE International Conference on
Software Testing, Verification and Validation (ICST'16), Chicago, IL, April
10-15, 2016, pp. 33-4
A comparison of forensic evidence recovery techniques for a windows mobile smart phone
<p>Acquisition, decoding and presentation of information from mobile devices is complex and challenging. Device memory is usually integrated into the device, making isolation prior to recovery difficult. In addition, manufacturers have adopted a variety of file systems and formats complicating decoding and presentation.</p>
<p>A variety of tools and methods have been developed (both commercially and in the open source community) to assist mobile forensics investigators. However, it is unclear to
what extent these tools can present a complete view of the information held on a mobile device, or the extent the results produced by different tools are consistent.</p>
<p>This paper investigates what information held on a Windows Mobile smart phone can be recovered using several different approaches to acquisition and decoding. The paper demonstrates that no one technique recovers all information of potential forensic interest from a Windows Mobile device; and that in some cases the information recovered is
conflicting.</p>
Classification of Online Customer Reviews for Digital Product Innovation: A Motivation Perspective
With rapid technological advances, digital products are becoming increasingly prevalent. Although past studies have examined the contribution of online reviews extensively in the context of physical products, there is limited understanding of the contribution of online reviews in digital product innovation. To this end, this study reviews previous work related to online reviews of physical and digital products in an attempt to reclassify online reviews of digital products from the perspective of consumer motivation. Taking game-related app reviews as an example, we employed a topic modeling model to extract insights related to consumer motivation. An in depth appreciation of consumers’ motivation from analyzing online reviews can yield invaluable insights in driving digital product innovation
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