Da Nang, Vietnam November 30 - December 3, 2020 https://elib.vku.udn.vn/handle/123456789/1005 2026-04-23T11:19:02Z 2026-04-23T11:19:02Z Nguyen, Quang Vu https://elib.vku.udn.vn/handle/123456789/1014 2021-03-08T07:49:00Z 2020-01-01T00:00:00Z

Title: Increasing Mutation Testing Effectiveness by Combining Lower Order Mutants to Construct Higher Order Mutants Authors: Nguyen, Quang Vu Abstract: Researching and proposing the solutions in the field of mutation testing in order to answer the question of how to improve the effectiveness of mutant testing is a problem that researchers, who study in the field of mutation testing, are interested. Limitations of mutation testing are really big problems that prevent its application in practice although this is a promising technique in assessing the quality of test data sets. The number of generated mutants is too large and easyto-kill mutants are two of those problems. In this paper, we have studied and presented our solution, as well as analyzed the empirical results for the purpose of introducing a way to improve the effectiveness of mutant testing. Instead of constructing higher order mutants by using and combining first-order mutants as previous studies, we propose a method to use higher-order mutants for creating mutants. In other words, we have combined two “lower” ordermutants to construct “higher” order mutants, i.e., use two second order mutants to construct a fourth order mutant, guided by our proposed objective and fitness functions. According to the experimental results, the number of generated is reduced and number of valuable mutants is fairly large, we have concluded that our approach seems to be a good way to overcome the main limitations of mutation testing. Description: Scientific Paper; Pages: 205-216

2020-01-01T00:00:00Z Phan, Hoang Viet Ninh, Khanh Duy Ninh, Khanh Chi https://elib.vku.udn.vn/handle/123456789/1013 2021-03-08T07:24:25Z 2020-01-01T00:00:00Z

Title: An Effective Vector Representation of Facebook Fan Pages and Its Applications Authors: Phan, Hoang Viet; Ninh, Khanh Duy; Ninh, Khanh Chi Abstract: Social networks have become an important part of human life. There have been recently several studies on using Latent Dirichlet Allocation (LDA) to analyze text corpora extracted from social platforms to discover underlying patterns of user data. However, when we wish to discover the major contents of a social network (e.g., Facebook) on a large scale, the available approaches need to collect and process published data of every person on the social network. This is against privacy rights as well as time and resource consuming. This paper tackles this problem by focusing on fan pages, a class of special accounts on Facebook that have much more impact than those of regular individuals. We proposed a vector representation for Facebook fan pages by using a combination of LDAbased topic distributions and interaction indices of their posts. The interaction index of each post is computed based on the number of reactions and comments, and works as the weight of that post in making of the topic distribution of a fan page. The proposed representation shows its effectiveness in fan page topic mining and clustering tasks when experimented on a collection of Vietnamese Facebook fan pages. The inclusion of interaction indices of the posts increases the fan page clustering performance by 9.0% on Silhouette score in the case of optimal number of clusters when using K-means clustering algorithm. These results will help us to build a system that can track trending contents on Facebook without acquiring the individual user’s data. Description: Scientific Paper; Pages: 674-685

2020-01-01T00:00:00Z Nguyen, Thanh Binh Mariem, Allagui Oum-El-Kheir, Aktou Ioannis, Parissis Le, Thi Thanh Binh https://elib.vku.udn.vn/handle/123456789/1012 2021-03-08T07:20:32Z 2020-01-01T00:00:00Z

Title: Experience Report on Developing a Crowdsourcing Test Platform for Mobile Applications Authors: Nguyen, Thanh Binh; Mariem, Allagui; Oum-El-Kheir, Aktou; Ioannis, Parissis; Le, Thi Thanh Binh Abstract: Crowdsourcing-based testing is a recent approach where testing is operated by volunteer users through the cloud. This approach is particularly suited for mobile applications since various users operating in various contexts can be involved. In the field of software engineering, crowd-testing has acquired a reputation for supporting the testing tasks, not only by professional testers, but also by end users. In this paper, we present TMACSTest (Testing of Mobile Applications using Crowdsourcing). This platform provides the important features for crowdsourcing testing of mobile apps by means of the following functionalities: It allows mobile app providers to register and upload mobile apps for testing, and it allows volunteering Internet users to register and test uploaded mobile apps. Expected behavior is that uploaded mobile apps are tested by many different Internet users in order to cover different runtime platforms and meaningful geographical locations. Description: Scientific Paper; Pages: 651-661

2020-01-01T00:00:00Z Nguyen, Ha Huy Cuong Nguyen, Duc Hien Nguyen, Van Loi Nguyen, Thanh Thuy https://elib.vku.udn.vn/handle/123456789/1011 2021-03-08T07:16:55Z 2020-01-01T00:00:00Z

Title: Smart Solution to Detect Images in Limited Visibility Conditions Based Convolutional Neural Networks Authors: Nguyen, Ha Huy Cuong; Nguyen, Duc Hien; Nguyen, Van Loi; Nguyen, Thanh Thuy Abstract: Decrease in visibility causes many difficulties in vision, tracking. Current classic object detection techniques do not give satisfying results in less visibility. It is essential to detect and recognize the objects under such conditions and devise a better object detection mechanism. The paper proposes a solution to this problem by using a multi step approach that uses Saliency techniques and modern object detection algorithms to obtain the desired results. The distorted image is enhanced via a deep neural network for visibility enhancement. The image frame of a better quality undergoes saliency techniques so that less visible objects are visible. Faster Region-based Convolutional Neural Network (R-CNN) then runs on the saliency output to yield bounding boxes for all the objects. The coordinates of the bounding boxes are then applied on the original image thus detecting all the objects in a distorted image with less visibility. Description: Scientific Paper; Pages: 641-650

2020-01-01T00:00:00Z