Quantum computers, leveraging superposition and entanglement, offer significant qubit efficiency for data processing compared to classical systems. However, encoding classical data into quantum states, given the current limitations of quantum hardware, often results in higher runtime complexity than classical methods, thus limiting the perceived quantum advantage. Previous quantum data compression methods, primarily based on Amplitude Encoding and mixed-state systems, result in lossy data recovery and necessitate extensive preprocessing. In this work, we propose Quantum Run-Length Encoding (QRLE), a novel lossless quantum data compression method that integrates Basic Encoding with Run-Length Encoding principles. By encoding repeated data sequences with their run lengths, QRLE achieves efficient and accurate data recovery on quantum computers, while exponentially reducing both qubit costs and runtime complexity compared to existing quantum data storage models. We further explore QRLE’s application in image processing, where it significantly optimizes quantum resource utilization over recent quantum image representation techniques. Experiments conducted on both quantum simulators and IBM’s superconducting quantum computer validate the efficiency of QRLE and confirm its compatibility with current quantum hardware.

Change-level defect prediction, also known as just-in-time (JIT) defect prediction, concentrates on predicting if a specific commit is likely to introduce defects. It effectively alleviates the limitations of traditional file-level defect prediction techniques, such as coarse-grained, hard to trace and poor timeliness. Currently, most change-level defect prediction techniques construct defect prediction models by using either expert features or semantic features. Recent studies have shown that the defect prediction performance can be enhanced by integrating these two types of features. However, obtaining expert features is not an easy task, due to missing historical data in real projects. To address the aforementioned problem, this paper proposes TS-SDP (Teacher–Student based Software Defect Prediction) based on teacher–student network. First, the source code is analyzed to extract expert features and semantic features. Then, a teacher–student network framework is constructed. In this framework, both features are used as inputs to the teacher network and only semantic features are used as inputs to the student network. The student network is enabled to learn about the expert features from the teacher network through the loss function. Finally, the student network is used to differentiate commits that are defect-inducing and those that are not, in the presence of only semantic features. The results of the experiments carried out on a dataset containing 21 different projects show that, when only semantic features are available, the cross-network knowledge dissemination between the teacher and student network makes it possible to predict defects. When compared to the state-of-the-art change-level defect prediction method, JIT-Fine, TS-SDP is 0.130, 0.114, 0.123 and 0.016 greater in [Formula: see text], [Formula: see text], F-measure and [Formula: see text], respectively.

Deep learning has emerged as a promising technology for achieving Android malware detection. To further unleash its detection potentials, software visualization can be integrated for analyzing the details of app behaviors clearly. However, facing increasingly sophisticated malware, existing visualization-based methods, analyzing from one or randomly-selected few views, can only detect limited attack types. We propose and implement LensDroid, a novel technique that detects Android malware by visualizing app behaviors from multiple complementary views. Our goal is to harness the power of combining deep learning and software visualization to automatically capture and aggregate high-level features that are not inherently linked, thereby revealing hidden maliciousness of Android app behaviors. To thoroughly comprehend the details of apps, we visualize app behaviors from three related but distinct views of behavioral sensitivities, operational contexts and supported environments. We then extract high-order semantics based on the views accordingly. To exploit semantic complementarity of the views, we design a deep neural network based model for fusing the visualized features from local to global based on their contributions to downstream tasks. A comprehensive comparison with six baseline techniques is performed on datasets of more than 51K apps in three real-world typical scenarios, including overall threats, app evolution and zero-day malware. The experimental results show that the overall effectiveness of LensDroid is better than the baseline techniques. We also validate the complementarity of the views and demonstrate that the multi-view fusion in LensDroid enhances Android malware detection.

Quantum computing offers parallel processing capabilities and resource-saving advantages, particularly useful for managing expansive datasets and complex image processing tasks. Grayscale images, being the simplest single-channel image mode, are frequently employed in artificial intelligence training. Before actual image applications, various image processing operations are typically required. However, the restoration of a grayscale image of dimensions 2n× 2nafter a series of linear transformations poses a challenge. Existing methods typically involve finding the inverse of the most recent linear transformation or re-encoding the image followed by repeated operations until the final transformation, resulting in excessive computational overhead and disconnection from subsequent quantum grayscale image applications. To address this issue, we propose a universal quantum linear restoration algorithm for grayscale image, denoted as QLR, which effectively bridges the stages of linear transformation and subsequent image applications. QLR reduces the time complexity from O(2n) to O(n) compared to classical counterpart. Building upon the QLR algorithm, we further propose two quantum resource-optimized compression methods for optional lossless image storage. Combining with other quantum algorithms and techniques, we design a framework (QGIP) aimed at bridging the processes of quantum grayscale image processing and applications. Experiments simulated on the IBM Quantum platform validate the correctness and efficiency of our proposal.

Due to the widespread application and economic value of smart contracts, they have become targets for attackers, leading to significant economic losses from vulnerabilities. Therefore, it is crucial to detect potential vulnerabilities in smart contracts before they are deployed. However, existing machine learning approaches often overlook the type information of nodes and edges, while those based on heterogeneous graphs only utilize the semantic information of smart contracts, neglecting the syntax structure information. This oversight compromises the performance in detecting vulnerabilities. To address these issues, we propose a novel smart contract vulnerability detection approach named HG-Detector(Heterogeneous Graph Detector), which stands for Heterogeneous Graph Detector. This approach integrates semantic and syntax structure information by employing a heterogeneous graph neural network to analyze the source code of smart contracts. It extracts both semantic and syntax structure information and then uses a classifier to detect potential vulnerabilities. Experimental results on a dataset comprising 1269 smart contracts show that, compared to MANDO, HG-Detector has achieved an average increase of 10.06% in Precision, an average increase of 1.61% in Recall, an average increase of 2.29% in the F1, and an average increase of 4.78% in Accuracy across seven types of vulnerabilities

Automated fault localization methods can expedite the process for developers to locate faulty code in complex software systems. Existing fault localization methods improve performance by combining the suspicious scores from different kinds of fault localization methods. Among these, the suspicious scores of mutation-based fault localization methods, commonly referred as mutation features, have been proven to effectively enhance fault localization performance. However, collecting mutation features requires generating a large number of mutants and executing test cases for each mutant, which demands sub-stantial computational resources and time. Additionally, certain code statements lack mutation features because no mutant can be generated for them, which affect the performance of fault localization. To address this, this paper proposes a Teacher and Student network-based Fault Lecalization (TS-FL) method. Firstly, a BiLSTM-based classifier is used to extract the deep semantic features of code statements, and the suspicious scores calculated by spectrum-based and mutation-based fault localization methods are used as the spectrum features and mutation features of the code statements, respectively. Then, a teacher-student network is constructed, and a mutual learning strategy is used to collaboratively train the teacher and student network, enabling the student network to learn the mutation feature information from the teacher network and thereby enhance its fault localization performance. The experimental results on Defects4J show that, without using mutation features, TS-FL can locate 36, 36, and 35 more faulty statements than spectrum-based fault localization methods Ochiai, Tarantula, and DStar, and can locate 8 more faulty statements than deep learning-based fault localization method TRANSFER-FL, in terms of Top-1.