Automated testing of Android apps is essential for app users, app developers, and market maintainer communities alike Given the widespread adoption of Android and the specificities of its development model, the literature has proposed various testing approaches for ensuring that not only functional requirements but also nonfunctional requirements are satisfied In this paper, 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 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

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Automated Testing of Android Apps: A Systematic Literature Review

In one embodiment, a method includes collecting communications from a plurality of sources. The method further includes selecting a set of topics and a set of users. In addition, the method includes, for each user, identifying, from the collected communications, conversations in which the user has participated. Moreover, the method includes, for each topic, for each user: measuring a proportion of the identified conversations that contain content suggestive of the topic; analyzing timing of the identified conversations; and examining relationships among data attributes of the identified conversations. Further, the method includes generating multidimensional expertise data. The multidimensional expertise data is representative of the user's expertise on the topic. The multidimensional expertise data includes a topical dimension, an expertise-scope dimension, and a timeline dimension. The method also includes, for each topic, ranking the set of users by expertise index. Also, the method includes providing a searchable interface.

System and method for managing and identifying subject matter experts

Image segmentation is a challenging process in numerous applications. Region growing is one of the segmentation techniques as a basis for the Seeded Region Growing method. A novel real time integrated method was developed in the current work to locate the segmented region of interest of an image based on the Region Growing segmentation method along with the thresholding supported image segmentation. Through the proposed work, a homogeneity based on pixel intensity was suggested as well as the threshold value can be decided via a variety of schemes such as manual selection, Iterative method, Otsu’s method, local thresholding to obtain the best possible threshold. The experimental results were performed on different images obtained from an Alpert dataset. A comparative study was arried out with the human segmented image, threshold based region growing, and the proposed integrated method. The results established that the proposed integrated method outperformed the region growing method in terms of the recall and F-score. Although, it had comparable recall values with that gained by the human segmented images. It was noted that as the image under test had a dark background with the brighter object, thus the proposed method provided the superior recall value compared to the other methods.

http://ijece.iaescore.com/index.php/IJECE/article/download/5889/5549

An Integrated Interactive Technique for Image Segmentation using Stack based Seeded Region Growing and Thresholding

In one embodiment, a method includes providing an a priori classification engine, an a posteriori classification engine, and a heuristics engine. The a priori classification engine is operable to perform an a priori classification. The a posteriori classification engine is operable to perform an a posteriori classification. The heuristics engine is operable to perform a heuristics classification. In addition, the method includes accessing data from at least one source. The method further includes, responsive to an indication that the a priori classification should be performed, performing the a priori classification on the data. The method also includes, responsive to an indication that the a posteriori classification should be performed, performing the a posteriori classification on the data. Further, the method includes, responsive to an indication that the heuristics classification should be performed, performing the heuristics classification on the data.

System and method for classifying data

A Pareto-based evolutionary multiobjective approach is adopted to optimize the functionals in the trace transform (TT) for extracting image features that are robust to noise and invariant to geometric deformations such as rotation, scale, and translation (RST). To this end, sample images with noise and with RST distortion are employed in the evolutionary optimization of the TT, which is termed evolutionary TT with noise (ETTN). Experimental studies on a fish image database and the Columbia COIL-20 image database show that the ETTN optimized on a few low-resolution images from the fish database can extract robust and RST invariant features from the standard images in the fish database as well as in the COIL-20 database. These results demonstrate that the proposed ETTN is very promising in that it is computationally efficient, invariant to RST deformation, robust to noise, and generalizable.

Evolutionary Multiobjective Image Feature Extraction in the Presence of Noise

The invention provides a method and clinical data mining system for enabling a user to derive knowledge from data corresponding to a plurality of electronic health records stored in a repository. One or more data elements are provided as an input. The data elements may include textual reports, images, and one or more criteria specified by the user. Information is extracted from one or more images associated with one or more electronic health records stored in the repository, based on the data elements. Further, information is extracted from one or more textual reports and structured data associated with the one or more electronic health records. Thereafter, one or more reports are generated based on the extracted information to enable the user to analyze the information. Subsequently, the user may derive knowledge from the data based on the analysis.

Method and system for performing clinical data mining

Segmentation of medical images using seeded region growing technique is increasingly becoming a popular method because of its ability to involve high-level knowledge of anatomical structures in seed selection process Region based segmentation of medical images are widely used in varied clinical applications like visualization, bone detection, tumor detection and unsupervised image retrieval in clinical databases As medical images are mostly fuzzy in nature, segmenting regions based intensity is the most challenging task In this paper, we discuss about popular seeded region grow methodology used for segmenting anatomical structures in CT Angiography images We have proposed a gradient based homogeneity criteria to control the region grow process while segmenting CTA images

Gradient Based Seeded Region Grow method for CT Angiographic Image Segmentation

Due to increased adoption of digital inclusion in various businesses, location based services are gaining importance to provide value-added services for their customers. In this work, we present a computer vision based system for tracking customer locations by recognizing individual shopping carts inside shopping malls in order to facilitate location based services. We provide an efficient approach for cart recognition that consists of two stages: cart detection and then cart recognition. A binary pattern is placed between two pre-defined color markers and attached to each cart for recognition. The system takes live video feed as input from the cameras mounted on the aisles of the shopping mall and processes frames in real-time. In the cart detection stage, color segmentation, feature extraction and classification are used for detection of binary pattern along with color markers. In recognition stage, segmented binary strip is processed using spatial image processing techniques to decode the cart identification number.

Video analytics solution for tracking customer locations in retail shopping malls

Application testing is an integral part of the application life cycle. This testing effort is more for the 3rd party applications in the mobile phone market, due to the wide number of handsets available on which the application needs to be tested before being released. At the same time, majority of the applications use the cellular network, necessitating the tester1 (along with the handset) to be present in the service area of the cellular network. We present a remote testing system, wherein the handset is in the cellular network service area, but the tester is present in a remote location. The tester controls the handset over the Internet. This gives opportunities to leverage the potential of the global outsourcing business model in mobile application testing domain. In addition, the system is agnostic to the Operating System a application running on the mobile phone, and is also non-intrusive. Further, we present preliminary results on automating this remote testing process itself.

An Innovative System for Remote and Automated Testing of Mobile Phone Applications

In this paper, we propose an approach for representing both shape and texture information in an image using a single hybrid feature descriptor for Content Based Image Retrieval. Towards this, we compute the gradient magnitude of the input image prior to deriving features. Feature extraction is then performed using the responses from a bank of Gabor filters. Here, we exploit the fact that shape corresponds to the high spatial frequency content in the image whereas natural texture information predominantly lies within low to mid-range frequencies. This approach helps in better localization of characteristic texture as well as shape, due to spread of energy towards high frequencies in spectral domain. Moment invariants are extracted from Gabor filter responses which yield better retrieval performance than conventional statistical features. Experimental results show that this approach has relatively improved retrieval performance on Corel image data set when compared with recent approaches in the literature. Further experiments were also performed on a medical image dataset with 95.4 percent precision and 74.6 percent recall.

Harikrishna G. N. Rai

Papers

Method and system for performing clinical data mining

Gradient Based Seeded Region Grow method for CT Angiographic Image Segmentation

Video analytics solution for tracking customer locations in retail shopping malls

An Innovative System for Remote and Automated Testing of Mobile Phone Applications

Hybrid feature to encode shape and texture for Content Based Image Retrieval