Abstract-Automatic test case generation is a key ingredient of an efficient and cost-effective software verification process. In this paper we focus on testing applications that interact with the users through a GUI, and present AutoBlackTest, a technique to automatically generate test cases at the system level. AutoBlackTest uses reinforcement learning, in particular Q-Learning, to learn how to interact with the application under test and stimulate its functionalities. The empirical results show that AutoBlackTest can execute a relevant portion of the code of the application under test, and can reveal previously unknown problems by working at the system level and interacting only through the GUI.

By leveraging large clusters of commodity hardware, the Cloud offers great opportunities to optimize the operative costs of software systems, but impacts significantly on the reliability of software applications. The lack of control of applications over Cloud execution environments largely limits the applicability of state-of-the-art approaches that address reliability issues by relying on heavyweight training with injected faults.In this paper, we propose LOUD, a lightweight fault localization approach that relies on positive training only, and can thus operate within the constraints of Cloud systems. LOUD relies on machine learning and graph theory. It trains machine learning models with correct executions only, and compensates the inaccuracy that derives from training with positive samples, by elaborating the outcome of machine learning techniques with graph theory algorithms. The experimental results reported in this paper confirm that LOUD can localize faults with high precision, by relying only on a lightweight positive training.

Software libraries implement APIs that deliver reusable functionalities. To correctly use these functionalities, software applications must satisfy certain correctness policies, for instance policies about the order some API methods can be invoked and about the values that can be used for the parameters. If these policies are violated, applications may produce misbehaviors and failures at runtime. Although this problem is general, applications that incorrectly use API methods are more frequent in certain contexts. For instance, Android provides a rich and rapidly evolving set of APIs that might be used incorrectly by app developers who often implement and publish faulty apps in the marketplaces. To mitigate this problem, we introduce the novel notion of proactive library, which augments classic libraries with the capability of proactively detecting and healing misuses at run- time. Proactive libraries blend libraries with multiple proactive modules that collect data, check the correctness policies of the libraries, and heal executions as soon as the violation of a correctness policy is detected. The proactive modules can be activated or deactivated at runtime by the users and can be implemented without requiring any change to the original library and any knowledge about the applications that may use the library. We evaluated proactive libraries in the context of the Android ecosystem. Results show that proactive libraries can automati- cally overcome several problems related to bad resource usage at the cost of a small overhead.Comment: O. Riganelli, D. Micucci and L. Mariani, "Policy Enforcement with Proactive Libraries" 2017 IEEE/ACM 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS), Buenos Aires, Argentina, 2017, pp. 182-19

Many applications are implemented as multi-tier software systems, and are executed on distributed infrastructures, like cloud infrastructures, to benefit from the cost reduction that derives from dynamically allocating resources ondemand. In these systems, failures are becoming the norm rather than the exception, and predicting their occurrence, as well as locating the responsible faults, are essential enablers of preventive and corrective actions that can mitigate the impact of failures, and significantly improve the dependability of the systems. Current failure prediction approaches suffer either from false positives or limited accuracy, and do not produce enough information to effectively locate the responsible faults.In this paper, we present PreMiSE, a lightweight and precise approach to predict failures and locate the corresponding faults in multi-tier distributed systems. PreMiSE blends anomaly-based and signature-based techniques to identify multi-tier failures that impact on performance indicators, with high precision and low false positive rate. The experimental results that we obtained on a Cloud-based IP Multimedia Subsystem indicate that PreMiSE can indeed predict and locate possible failure occurrences with high precision and low overhead. Figure 1: The overall flow of PreMiSE online activities to predict failures ing failures that impact on performance indicators. As illustrated in Figure 1, PreMiSE -monitors the status of the system by collecting (a large set of) performance indicators from the system nodes, for instance CPU utilization for each CPU in the system, that we refer to as Key Performance Indicators (KPIs) (KPI monitoring in the figure), -identifies deviations from normal behaviors by pinpointing anomalous KPIs with anomaly-based techniques (Anomaly detection in the figure), -identifies incoming failures by identifying symptomatic anomalous KPI sets with signature-based techniques.(Signature-based failure prediction in the figure).

SUMMARYTesting GUI‐based applications is hard and time consuming because it requires exploring a potentially huge execution space by interacting with the graphical interface of the applications. Manual testing can cover only a small subset of the functionality provided by applications with complex interfaces, and thus, automatic techniques are necessary to extensively validate GUI‐based systems. This paper presents AutoBlackTest, a technique to automatically generate test cases at the system level. AutoBlackTest uses reinforcement learning, in particular Q‐learning, to learn how to interact with the application under test and stimulate its functionalities. When used to complement the activity of test designers, AutoBlackTest reuses the information in the available test suites to increase its effectiveness. The empirical results show that AutoBlackTest can sample better than state of the art techniques the behaviour of the application under test and can reveal previously unknown problems by working at the system level and interacting only through the graphical user interface. Copyright © 2014 John Wiley & Sons, Ltd.