Model dressing for automated exploratory testing

Automation of software testing is a complex problem with multiple facets to be handled in sync to be viable. In this work we propose two novel concepts; model dressing for automated exploratory testing. Model dressing maps an application under test to a model created for the domain of the application. In its simplest form, the domain model defines the business tasks as well as the user actions that can be carried out from the perspective of the end-user. Automated exploratory testing leverages the domain knowledge as well as the experience gained from testing applications in the same domain to test another application

Analytics on Anonymity for Privacy Retention in Smart Health Data

Advancements in smart technology, wearable and mobile devices, and Internet of Things, have made smart health an integral part of modern living to better individual healthcare and well-being. By enhancing self-monitoring, data collection and sharing among users and service providers, smart health can increase healthy lifestyles, timely treatments, and save lives. However, as health data become larger and more accessible to multiple parties, they become vulnerable to privacy attacks. One way to safeguard privacy is to increase users’ anonymity as anonymity increases indistinguishability making it harder for re-identification. Still the challenge is not only to preserve data privacy but also to ensure that the shared data are sufficiently informative to be useful. Our research studies health data analytics focusing on anonymity for privacy protection. This paper presents a multi-faceted analytical approach to (1) identifying attributes susceptible to information leakages by using entropy-based measure to analyze information loss, (2) anonymizing the data by generalization using attribute hierarchies, and (3) balancing between anonymity and informativeness by our anonymization technique that produces anonymized data satisfying a given anonymity requirement while optimizing data retention. Our anonymization technique is an automated Artificial Intelligent search based on two simple heuristics. The paper describes and illustrates the detailed approach and analytics including pre and post anonymization analytics. Experiments on published data are performed on the anonymization technique. Results, compared with other similar techniques, show that our anonymization technique gives the most effective data sharing solution, with respect to computational cost and balancing between anonymity and data retention

Makine öğrenmesi ile mobil uygulama sınıflandırılması ve otomatik keşif testi (Mobile application classification using machine learning and automated exploratory testing)

The knowledge of the business domain of a Software-Under-Test (SUT) is crucial for testing. Therefore identification of business domain and the underlying business processes is the basis for automated testing. Test cases and test input set can be automatically generated depending on the domain and process information. In this research, we apply machine learning techniques to determine the similarity of applications. Applications in the same domain should be highly similar and we can say that, same business processes are implemented in the applications of a business domain. Our hypothesis argues that assuming we can create a generalized Finite State Machine (FSM) model of a business domain, the states and transitions of the FSM could be matched to the business processes of a business domain. Previously created test cases and test input could be used for testing an application that is coherent with the states and transitions of the formal model. In this research we coin two novel terms,Model Dressing and Automated Exploratory Testing. Model dressing is matching an application to the generalized model of a business domain. Automated exploratory testing is using the previously gathered business domain knowledge to test new applications and gradually merging outcome to the previous know-how to improve testing process