Crowdsourced testing has gained prominence in the field of software testing due to its ability to effectively address the challenges posed by the fragmentation problem in mobile app testing. The inherent openness of crowdsourced testing brings diversity to the testing outcome. However, it also presents challenges for app developers in inspecting a substantial quantity of test reports. To help app developers inspect the bugs in crowdsourced test reports as early as possible, crowdsourced test report prioritization has emerged as an effective technology by establishing a systematic optimal report inspecting sequence. Nevertheless, crowdsourced test reports consist of app screenshots and textual descriptions, but current prioritization approaches mostly rely on textual descriptions, and some may add vectorized image features at the image-as-a-whole level or widget level. They still lack precision in accurately characterizing the distinctive features of crowdsourced test reports. In terms of prioritization strategy, prevailing approaches adopt simple prioritization based on features combined merely using weighted coefficients, without adequately considering the semantics, which may result in biased and ineffective outcomes. In this paper, we proposeEncrePrior, an enhanced crowdsourced test report prioritization approach via image-and-text semantic understanding and feature integration.EncrePriorextracts distinctive features from crowdsourced test reports. For app screenshots,EncrePriorconsiders the structure (i.e., GUI layout) and the contents (i.e., GUI widgets), viewing the app screenshot from the macroscopic and microscopic perspectives, respectively. For textual descriptions,EncrePriorconsiders the Bug Description and Reproduction Step as the bug context. During the prioritization, we do not directly merge the features with weights to guide the prioritization. Instead, in order to comprehensively consider the semantics, we adopt a prioritize-reprioritize strategy. This practice combines different features together by considering their individual ranks. The reports are first prioritized on four features separately. Then, the ranks on four sequences are used to lexicographically reprioritize the test reports with an integration of features from app screenshots and textual descriptions. Results of an empirical study show thatEncrePrioroutperforms the representative baseline approachDeepPriorby 15.61% on average, ranging from 2.99% to 63.64% on different apps, and the novelly proposed features and prioritization strategy all contribute to the excellent performance ofEncrePrior.

GUI testing has been prevailing in software testing. However, existing automated GUI testing tools mostly rely on frameworks of a specific platform. Testers have to fully understand platform features before developing platform-dependent GUI testing tools. Starting from the perspective of tester’s vision, we observe that GUIs on different platforms share commonalities of widget images and layout designs, which can be leveraged to achieve platform-independent testing. We propose UniRLTest, an automated software testing framework, to achieve platform independence testing. UniRLTest utilizes computer vision techniques to capture all the widgets in the screenshot and constructs a widget tree for each page. A set of all the executable actions in each tree will be generated accordingly. UniRLTest adopts a Deep Q-Network, a reinforcement learning (RL) method, to the exploration process and formalize the Android GUI testing problem to a Marcov Decision Process (MDP), where RL could work. We have conducted evaluation experiments on 25 applications from different platforms. The result shows that UniRLTest outperforms baselines in terms of efficiency and effectiveness.

With the development of wireless sensor networks (WSNs), WSNs have been widely used in various fields such as animal habitat detection, military surveillance, etc. This paper focuses on protecting the source location privacy (SLP) in WSNs. Existing algorithms perform poorly in non-uniform networks which are common in reality. In order to address the performance degradation problem of existing algorithms in non-uniform networks, this paper proposes a robust fixed path-based random routing scheme (RFRR), which guarantees the path diversity with certainty in non-uniform networks. In RFRR, the data packets are sent by selecting a routing path that is highly differentiated from each other, which effectively protects SLP and resists the backtracking attack. The experimental results show that RFRR increases the difficulty of the backtracking attack while safekeeping the balance between security and energy consumption.

Internet of Things (IoT) network is vulnerable to various cyberattacks, especially insider attacks. Most existing studies mainly detect nontargeted insider attackers, who manipulate all packets forwarded by them with a probability. Compared with nontargeted attackers, targeted attackers only manipulate specific packets, which makes them more efficient and covert. In this article, we propose a targeted insider attack model called conditional packets manipulation attack (CPMA), in which attackers maliciously manipulate the packets whose attribute values meet specific conditions with a probability. When resisting the CPMA attack, most existing detection algorithms are inefficient to find such malicious behavior. Also, they detect malicious nodes by collecting and analyzing the overall behavior of nodes, which are not appropriate for energy-constrained nodes in the IoT network. To solve these problems, we present CPMAED, a malicious nodes detection framework against CPMA attack. CPMAED maintains some partial trust metrics for each relay node, which indicate the probability of launch attacks when forwarding the packets with different attribute values. Also, our scheme leverages regression and clustering algorithms to evaluate the trust values of nodes and classify them into benign or malicious. In order to obtain higher detection accuracy, we optimize the routing of transmitted packets and inject the packets to collect more information about nodes to enhance detection. The experimental results show that our proposed scheme utilizing support vector machine and$K$-means can achieve good detection performance and identify malicious nodes’ attack modes with high accuracy.