Abstract-Use of mobile applications are trending these days due to adoption of handheld mobile devices with operating systems such as Android, iOS and Windows. Delivering quality mobile apps is as important as in any other web or desktop application. Simplification and ease of quality assurance or evaluation in mobile devices is achieved by using automated testing tools. These tools have been evaluated for their features, platforms, code coverage, and efficiency. However, they have not been evaluated and compared to each other for different quality attributes they can enhance in the apps under test. This research study aims to evaluate different testing tools focusing on identifying quality factors they aid to achieve in the apps under test. Furthermore, it aims to measure overall trends of essential quality factors achieved using automated testing tools. The findings of this study are beneficial to the practitioners and researchers. The practitioners need to look up for specific tools which aid them to assure the desired quality factors in the apps under test. The researchers may base their studies on the findings of this study to propose solutions or revise existing tools in order to achieve maximum number of critical quality attributes in the app under test. This study revealed that the trend of automated testing is high on usability, correctness and robustness. Moreover, the trend is average on testability and performance. However, for assurance of extensibility, maintainability, scalability, and platform compatibility, only a few tools are available.

Abstract.A malware (such as viruses, ransomware) is the main source of bringing serious security threats to the IT systems and their users now-adays. In order to protect the systems and their legitimate users from these threats, anti-malware applications are developed as a defense against malware. However, most of these applications detect malware based on signatures or heuristics that are still created manually and are error prune. Some recent applications employ data mining and machine learning techniques to detect malware automatically. However, such applications fail to classify them appropriately mainly because they suffer from high rate of false alarms on the one hand and being retrospective, fail to detect new unknown threats and variants of known malware on the other hand. Since anti-malware vendors receive a huge number of malware samples every day, there is an urgent need for malware analysis tools that can automatically detect malware rigorously, i.e. eliminating false alarms. To address these issues and challenges of current malware detection and analysis approaches, we propose a novel, open source and extensible platform (based on set of tools) that allows to combine various malware detection techniques to automatically detect/classify a malware more rigorously. The developed platform can be fed with malware samples from different providers and will enable the development of effective classification schemes and methods, which are not sufficiently effective without collaboration and the related sample aggregation. Furthermore, such collaborative platforms in cybersecurity enable efficient sharing of information (e.g., about new identified threats) to all collaborators and sharing of appropriate defences against them, if such defences exist.