Deep Reinforcement Learning for Black-box Testing of Android Apps

The state space of Android apps is huge, and its thorough exploration during testing remains a significant challenge. The best exploration strategy is highly dependent on the features of the app under test. Reinforcement Learning (RL) is a machine learning technique that learns the optimal strategy to solve a task by trial and error, guided by positive or negative reward, rather than explicit supervision. Deep RL is a recent extension of RL that takes advantage of the learning capabilities of neural networks. Such capabilities make Deep RL suitable for complex exploration spaces such as one of Android apps. However, state-of-the-art, publicly available tools only support basic, Tabular RL. We have developed ARES, a Deep RL approach for black-box testing of Android apps. Experimental results show that it achieves higher coverage and fault revelation than the baselines, including state-of-the-art tools, such as TimeMachine and Q-Testing. We also investigated the reasons behind such performance qualitatively, and we have identified the key features of Android apps that make Deep RL particularly effective on them to be the presence of chained and blocking activities. Moreover, we have developed FATE to fine-tune the hyperparameters of Deep RL algorithms on simulated apps, since it is computationally expensive to carry it out on real apps

APPregator: A Large-Scale Platform for\ua0Mobile Security Analysis

The Google Play Store currently includes up to 2.8M apps. Nonetheless, it is rather straightforward for a user to quickly retrieve the app that matches her tastes, as Google provides a reliable search engine. However, it is likewise almost impossible to select apps according to a security footprint (e.g., all apps that enforce SSL pinning). To overcome this limitation, this paper presents APPregator, a platform which allows security analysts to i) download apps from multiple app stores, ii) perform automated security analysis (both static and dynamic), and iii) aggregate the results according to user-defined security constraints (e.g., vulnerability patterns). The empirical assessment of APPregator on a set of 200.000 apps taken from the Google Play Store and Aptoide suggests that the current implementation grants a good level of performance and reliability. APPregator will be made freely available to the research community by the end of 2020

IFRIT: Focused Testing through Deep Reinforcement Learning

Software is constantly changing as developers add new features or make changes. This directly impacts the effectiveness of the test suite associated with that software, especially when the new modifications are in an area where no test case exists. This article addresses the issue of developing a high-quality test suite to repeatedly cover a given point in a program, with the ultimate goal of exposing faults affecting the given program point. Our approach, IFRIT, uses Deep Reinforcement Learning to generate diverse inputs while keeping a high level of reachability of the desired program point. WRIT achieves better results than state-of-the-art and baseline tools, improving reachability, diversity and fault detection

COSMO: Code Coverage Made Easier for Android

The degree of code coverage reached by a test suite is an important indicator of the thoroughness of testing. Most coverage tools for Android apps work at the bytecode level and provide no information to developers about which source code lines have not yet been exercised by any test case. In this paper, we present COSMO, the first fully automated Android app instrumenter publicly available that operates at the source code level in a completely transparent way, making it fully compatible with existing system level testing technologies and Android test generators. The experiments that we have conducted on a large benchmark of Android apps show that COSMO can successfully instrument most apps without altering their execution traces, introducing a small, acceptable runtime overhead

Bioresource Technology 100 (2009) 3740–3744 Contents lists available at ScienceDirect

journal homepage: www.elsevier.com/locate/biortech Energy valorization of industrial biomass: Using a batch frying proces

Mealworm larvae production systems: management scenarios

International audienceThis chapter highlights a part of the work carried out within the framework of the DESIRABLE project ("DESIgning the Insect bioRefinery to contribute to a more sustainABLE agro-food industry"), funded by the French National Research Agency (ANR). Here, our aim is to present original research results to operators willing to implement insect-based value-chains for feed, and to decision-makers eager to understand the main related stakes. Our tasks focused on the practical organization of mealworm larvae (T. molitor) raising and processing, in middle-sized (about 400 tons of larvae per year) and very large-sized (about 2000 tons per year) processing systems. The objective was to monitor health hazards and to organize production chains in the best way possible, in order to make human operations smooth and efficient, while accounting for the physiological needs of insects. In this chapter, we have designed in detail relevant insect "group management" for middlesized farming systems, some being focused on farrow-to-wean stage, and others specialized in insect fattening. We highlight improvement avenues, which would deserve additional developments in the future. For very large-sized production systems, we suggest adequate group management, and we identify the technical difficulties which hamper the setting-up of such huge integrated systems, to date. We present how we have established three different kinds of processes for an annual production of 10,000 tons, from larvae to flour. We also present the features of intermediate by-products, by generating data evaluating the flows of energy and matter, thus leading the way towards a possible economic feasibility. We raise some remaining questions to be explored. We also provide directions for environmental and economic evaluation. These results show the way for future scientific investigations, in accordance with sound social concerns. In part "Insects as animal feed"

Effect of solarization on the removal of indicator microorganisms from municipal sewage sludge

The effect of solarization on bacterial inactivation in sewage sludge was studied using thermotolerant coliforms, enterococci and Escherichia coli (E. coli) as the indicator organisms. Solarization significantly increased the sludge temperature. The maximum temperatures were achieved at the beginning of the second week, reaching 65, 58, 55 and 50°C at depths of 0-10, 10-20, 20-30 and 30-40 cm, respectively. E. coli was found to be the most sensitive microorganism and was reduced to undetectable levels after 9 d at all monitored sludge depths. Thermotolerant coliforms were rapidly inactivated but were not reduced to below the detection limit. The inactivation curves of enterococci showed both shoulders and tailing, indicating a larger heat resistant fraction than with E. coli and the thermotolerant coliforms. Overall, the results suggest that the temperature regime produced by solarization was sufficient to reduce bacterial indicators to an acceptable level, meeting the pathogen regulation limit, in two weeks. © 2013 Taylor and Francis Group, LLC