Verifying Web Applications: From Business Level Specifications to Automated Model-Based Testing

One of reasons preventing a wider uptake of model-based testing in the industry is the difficulty which is encountered by developers when trying to think in terms of properties rather than linear specifications. A disparity has traditionally been perceived between the language spoken by customers who specify the system and the language required to construct models of that system. The dynamic nature of the specifications for commercial systems further aggravates this problem in that models would need to be rechecked after every specification change. In this paper, we propose an approach for converting specifications written in the commonly-used quasi-natural language Gherkin into models for use with a model-based testing tool. We have instantiated this approach using QuickCheck and demonstrate its applicability via a case study on the eHealth system, the national health portal for Maltese residents.Comment: In Proceedings MBT 2014, arXiv:1403.704

Investigating the contextual requirements of the Juster Scale

Researchers have employed the Juster Scale to collect purchase probability data with notable success. Reviewing the Juster Scale studies, however, has revealed that there is considerable variation in its per¬formance. Some of these variations appeared to be caused by the context in which the Juster Scale has been presented to respondents. This paper discusses three factors that influence the context of the Juster Scale and reports the results of a study that attempted to standardise its contextual requirements. The results substantiate further the Juster Scale's satisfactory performance in collecting purchase probability data

FraudDroid: Automated Ad Fraud Detection for Android Apps

Although mobile ad frauds have been widespread, state-of-the-art approaches in the literature have mainly focused on detecting the so-called static placement frauds, where only a single UI state is involved and can be identified based on static information such as the size or location of ad views. Other types of fraud exist that involve multiple UI states and are performed dynamically while users interact with the app. Such dynamic interaction frauds, although now widely spread in apps, have not yet been explored nor addressed in the literature. In this work, we investigate a wide range of mobile ad frauds to provide a comprehensive taxonomy to the research community. We then propose, FraudDroid, a novel hybrid approach to detect ad frauds in mobile Android apps. FraudDroid analyses apps dynamically to build UI state transition graphs and collects their associated runtime network traffics, which are then leveraged to check against a set of heuristic-based rules for identifying ad fraudulent behaviours. We show empirically that FraudDroid detects ad frauds with a high precision (93%) and recall (92%). Experimental results further show that FraudDroid is capable of detecting ad frauds across the spectrum of fraud types. By analysing 12,000 ad-supported Android apps, FraudDroid identified 335 cases of fraud associated with 20 ad networks that are further confirmed to be true positive results and are shared with our fellow researchers to promote advanced ad fraud detectionComment: 12 pages, 10 figure