Line-level software defect prediction (LLDP) is crucial for locating and modifying defective code and has drawn increasing attention from both academic and industrial communities. Recently, a series of deep learning-based LLDP models have been proposed. However, there is no systematic comparison of these LLDP models. In this paper, we aim to compare the performance of these models, evaluate their consistency in different scenarios, and investigate whether fusing handcrafted features can improve the prediction performance. To this end, we conducted a comprehensive empirical study on eight recent state-of-the-art models using 32 benchmark datasets from nine software projects’ releases. We evaluated them in both cross-version and cross-project scenarios with four widely used performance metrics, including AUC, Recall@Top20%LOC, Effort@Top20%Recall, and IFA. Moreover, we investigated the impact of handcrafted features on LLDP models based on two different fusion strategies. The results show that 1) the difference among these models is statistically significant; 2) no model is always superior across all performance metrics but Bugsplorer generally performs best except IFA; 3) model performance rankings are inconsistent between cross-version and cross-project scenarios, revealing that a model’s effectiveness is highly scenario-dependent; 4) furthermore, fusing handcrafted features significantly improves model prediction performance, and the fusion strategy also matters. In conclusion, the selection of an LLDP model should be guided by specific scenarios and performance metrics, and the hybrid model that combines deep learning representations with handcrafted features is a promising alternative for LLDP.

Cross-project defect prediction (CPDP) uses the historical defect dataset collected from source projects to train a model and then applies it to the target project. However, almost all existing CPDP methods are developed for offline scenarios where the trained models are fixed and cannot be updated along with the incoming labeled target instances after training. Actually, the label of target instances usually arrives online in a streaming manner which can be used to update CPDP models for better defect prediction performance on the next unlabeled target instance. To bridge these gaps, we propose a novel effective online cross-project defect prediction framework named ALOGO. ALOGO includes two essential phases: offline cross-project defect prediction phase and online within-project defect prediction (WPDP) phase which are combined by an adaptive weighted adjustment mechanism. In the offline CPDP phase, the global offline defect knowledge is learned by minimizing the difference between the source and target datasets based on an offline CPDP model. In the online WPDP phase, the local online defect knowledge is learned based on an online WPDP model. These two kinds of defect knowledge are then combined to obtain the latest and most valuable defect knowledge. Experimental results on 27 defect datasets show that ALOGO improves the performance over the existing state-of-the-art online CPDP model by 31.2% in terms of the Matthews correlation coefficient (MCC) and also outperforms the baseline in terms of other four well-known measures. It can be concluded that 1) it is necessary to consider building online CPDP models; 2) ALOGO is a more promising alternative for online CPDP.

This article investigates the adaptive resource allocation scheme for digital twin (DT) synchronization optimization over dynamic wireless networks. In our considered model, a base station (BS) continuously collects factory physical object state data from wireless devices to build a real-time virtual DT system for factory event analysis. Due to continuous data transmission, maintaining DT synchronization must use extensive wireless resources. To address this issue, a subset of devices is selected to transmit their sensing data, and resource block (RB) allocation is optimized. This problem is formulated as a constrained Markov process (CMDP) problem that minimizes the long-term mismatch between the physical and virtual systems. To solve this CMDP, we first transform the problem into a dual problem that refines RB constraint impacts on device scheduling strategies. We then propose a continual reinforcement learning (CRL) algorithm to solve the dual problem. The CRL algorithm learns a stable policy across historical experiences for quick adaptation to dynamics in physical states and network capacity. Simulation results show that the CRL can adapt quickly to network capacity changes and reduce normalized root mean square error (NRMSE) between physical and virtual states by up to 55.2%, using the same RB number as traditional methods.

To achieve continuous massive data transmission with significantly reduced data payload, the users can adopt semantic communication techniques to compress the redundant information by transmitting semantic features instead. However, current works on semantic communication mainly focus on high compression ratio, neglecting the wireless channel effects including dynamic distortion and multi-user interference, which significantly limit the fidelity of semantic communication. To address this, this paper proposes a diffusion model (DM)-based channel enhancer (DMCE) for improving the performance of multi-user semantic communication, with the DM learning the particular data distribution of channel effects on the transmitted semantic features. In the considered system model, multiple users (such as road cameras) transmit semantic features of multi-source data to a receiver by applying the joint source-channel coding (JSCC) techniques, and the receiver fuses the semantic features from multiple users to complete specific tasks. Then, we propose DMCE to enhance the channel state information (CSI) estimation for improving the restoration of the received semantic features. Finally, the fusion results at the receiver are significantly enhanced, demonstrating a robust performance even under low signal-to-noise ratio (SNR) regimes, enabling the generation of effective object segmentation images. Extensive simulation results with a traffic scenario dataset show that the proposed scheme can improve the mean Intersection over Union (mIoU) by more than 25% at low SNR regimes, compared with the benchmark schemes.

With the escalating demand for computing power driven by the advancements in deep learning and artificial intelligence (AI), the computing power networks serve as critical infrastructure nowadays. Recently, the emerging blockchain enhanced computing power networks has showed appealing advantages of fair and reliable resource allocation, adaptive and flexible management on heterogeneous computing power network, transparent and trustworthy transactions, thereby alleviating the drawbacks of traditional computing infrastructure. Yet, the efficiency and security concerns associated with blockchain-based transactions are neglected when it is applied to computing power trading scenario. To address these issues, in this paper, we introduces the EFSC (Efficient, Flexible, Secure Computing-power-trading) system, a blockchain and smart contracts-based solution aimed at enhancing the efficiency and security of computing power trading. By leveraging a hybrid mechanism that combines on-chain and off-chain methods and utilizing off-chain transaction channels, the EFSC system achieves high throughput transaction processing while also supporting adjustable on-chain policies. Furthermore, transaction data templates based on the Verifiable Credentials (VC) data model enable flexible computing power transaction patterns, allowing providers to define and register configurable transaction initiation and settlement credential templates. To address transaction security concerns, the system leverages Decentralized Identifiers (DID) for off-chain channel access authentication and utilizes DID and VC mechanisms to facilitate transaction initiation and settlement processing, thereby enhancing the credibility and authenticity of transaction data. The experimental results and analysis demonstrate the effectiveness of proposed system.

This paper studies an end-to-end video semantic communication system for massive communication. In the considered system, the transmitter must continuously send the video to the receiver to facilitate character reconstruction in immersive applications, such as interactive video conference. However, transmitting the original video information with substantial amounts of data poses a challenge to the limited wireless resources. To address this issue, we reduce the amount of data transmitted by making the transmitter extract and send the semantic information from the video, which refines the major object and the correlation of time and space in the video. Specifically, we first develop a video semantic communication system based on major object extraction (MOE) and contextual video encoding (CVE) to achieve efficient video transmission. Then, we design the MOE and CVE modules with convolutional neural network based motion estimation, contextual extraction and entropy coding. Simulation results show that compared to the traditional coding schemes, the proposed method can reduce the amount of transmitted data by up to 25% while increasing the peak signal-to-noise ratio (PSNR) of the reconstructed video by up to 14%.

This paper studies the problem of the lightweight image semantic communication system that is deployed on Internet of Things (IoT) devices. In the considered system model, devices must use semantic communication techniques to support user behavior recognition in ultimate video service with high data transmission efficiency. However, it is computationally expensive for IoT devices to deploy semantic codecs due to the complex calculation processes of deep learning (DL) based codec training and inference. To make it affordable for IoT devices to deploy semantic communication systems, we propose an attention-based UNet enabled lightweight image semantic communication (LSSC) system, which achieves low computational complexity and small model size. In particular, we first let the LSSC system train the codec at the edge server to reduce the training computation load on IoT devices. Then, we introduce the convolutional block attention module (CBAM) to extract the image semantic features and decrease the number of downsampling layers thus reducing the floating-point operations (FLOPs). Finally, we experimentally adjust the structure of the codec and find out the optimal number of downsampling layers. Simulation results show that the proposed LSSC system can reduce the semantic codec FLOPs by 14%, and reduce the model size by 55%, with a sacrifice of 3% accuracy, compared to the baseline. Moreover, the proposed scheme can achieve a higher transmission accuracy than the traditional communication scheme in the low channel signal-to-noise (SNR) region.

In this paper, we investigate codebook-based near-field beam training for extremely large-scale intelligent reflecting surface (XL-IRS). Compared with the conventional far-field beam training method that only searches for the best beam direction, the near-field beam training is more challenging since it requires a beam search over both the angular and distance domains due to the spherical wavefront propagation model. To reduce the near-field beam-training overhead of two-dimensional exhaustive search, we propose a novel two-layer codebook-based near-field beam training scheme that decomposes the two-dimensional search into two sequential phases. Specifically, the layer-l codebook designed based on the omnidirectivity of random-phase beam pattern is firstly employed to estimate the user distance. Then, given the estimated user distance of the layer-1, a customized layer-2 codebook is employed to scan the candidate locations of the user. Numerical results demonstrate that the proposed scheme can achieve more accurate estimation of the user distance and angle, as well as higher data rate with smaller training overhead, compared with benchmarks.

The explosion of data over the last decades puts significant strain on the computational capacity of the central processing unit (CPU), challenging online analytical processing (OLAP). While previous studies have shown the potential of using Field Programmable Gate Arrays (FPGAs) in database systems, integrating FPGA-based hardware acceleration with relational databases remains challenging because of the complex nature of relational database operations and the need for specialized FPGA programming skills. Additionally, there are significant challenges related to optimizing FPGA-based acceleration for specific database workloads, ensuring data consistency and reliability, and integrating FPGA-based hardware acceleration with existing database infrastructure. In this study, we proposed a novel end-to-end FPGA-based acceleration system that supports native SQL statements and storage engine. We defined a callback process to reload the database query logic and customize the scanning method for database queries. Through middleware process development, we optimized offloading efficiency on PCIe bus by scheduling data transmission and computation in a pipeline workflow. Additionally, we designed a novel five-stage FPGA microarchitecture module that achieves optimal clock frequency, further enhancing offloading efficiency. Results from systematic evaluations indicate that our solution allows a single FPGA card to perform as well as 8 CPU query processes, while reducing CPU load by 34%. Compared to using 4 CPU cores, our FPGA-based acceleration system reduces query latency by 1.7 times without increasing CPU load. Furthermore, our proposed solution achieves 2.1 times computation speedup for data filtering compared with the software baseline in a single core environment. Overall, our work presents a valuable end- to-end hardware acceleration system for OLAP databases.

Background:Multi-source cross-project defect prediction (MSCPDP) attempts to transfer defect knowledge learned from multiple source projects to the target project. MSCPDP has drawn increasing attention from academic and industry communities owing to its advantages compared with single-source cross-project defect prediction (SSCPDP). However, two main problems, which are how to effectively extract the transferable knowledge from each source dataset and how to measure the amount of knowledge transferred from each source dataset to the target dataset, seriously restrict the performance of existing MSCPDP models.Objective:In this paper, we propose a novel multi-source transfer weighted ensemble learning (MASTER) method for MSCPDP.Method:MASTER measures the weight of each source dataset based on feature importance and distribution difference and then extracts the transferable knowledge based on the proposed feature-weighted transfer learning algorithm. Experiments are performed on 30 software projects. We compare MASTER with the latest state-of-the-art MSCPDP methods with statistical test in terms of famous effort-unaware measures (i.e., PD, PF, AUC, and MCC) and two widely used effort-aware measures (Popt20% and IFA).Result:The experiment results show that: 1) MASTER can substantially improve the prediction performance compared with the baselines, e.g., an improvement of at least 49.1% in MCC, 48.1% in IFA; 2) MASTER significantly outperforms each baseline on most datasets in terms of AUC, MCC,Popt20% and IFA; 3) MSCPDP model significantly performs better than the mean case of SSCPDP model on most datasets and even outperforms the best case of SSCPDP on some datasets.Conclusion:It can be concluded that 1) it is very necessary to conduct MSCPDP, and 2) the proposed MASTER is a more promising alternative for MSCPDP.

To investigate the performance of different software defect number prediction (SDNP) models, we compared 22 SDNP models including six count models, five well-known single machine learning techniques, four ensemble learning techniques, four sampling techniques, and three hybrid techniques on 45 defect datasets in terms of fault-percentile-average (FPA), Popt, and root-mean-square-error (RMSE). The experimental results show that 1) count-model based SDNP models usually perform worst; 2) resampling, ensemble learning, and hybrid techniques are generally helpful for improving FPA and Popt; 3) SHSE outperforms other SDNP models in terms of FPA and Popt.

With the rapid development of web services, social media has been a prevalent and readily way for people to express themselves and share their daily lives. Consequently, numerous user-generated content is accumulated on social media platforms. These data usually contain rich information and knowledge for users, which is a viable source for user data mining. As one of the prevalent techniques in user data mining, mining personalized topics and discovering user preferences from social media data attract much interest in academic and industrial communities. The emerging Neural Topic Models(NTMs) have recently shown leading performance and scalability by employing neural networks. However, most existing NTMs usually model topics simply from observed document token information and do not explicitly take user preferences into the generative process, which inevitably fails to model personalized topics. To address this issue, we introduce Neural Personalized Topic Model(NPTM), a novel NTM that can discover personalized topics and user preferences. NPTM introduces a novel hybrid generative process for combining user preferences and contextualized document codes in modeling personalized topics. A transformer-based document encoder to obtain contextualized document codes. For user preference modeling, NPTM regards user-related information as trainable user embeddings, further determining user preferences over the topics. Following the proposed hybrid generative process, we present a module-wise asynchronous optimization strategy to get coherent topics and user preferences. Then, we apply our model to two challenging real-world social media post collections and compare them against several baseline methods to verify our contributions. The experimental results demonstrate the effectiveness of the proposed method.

In this paper, we investigate the semantic-aware efficient sampling policy for remote state estimation in a digital twin (DT) empowered smart factory with multiple wireless sensing devices and an edge server. In this setting, wireless sensing devices must continuously sample the factory states and transmit semantic-aware sensing data to the server. Using the received sensing data, the server builds a realtime DT mapping remotely that analyzes and predicts the events in the factory. Since the DT requires continuous data transmission, maintaining the DT inevitably consumes significant amounts of limited wireless resources. To address this issue, we reduce the required amount of data transmission by making wireless devices only send the semantic-aware sensing data that indicates the occurrence of events, otherwise stay idle. In particular, we first invoke the age of incorrect information (AoII) to measure the semantic of the sensing data, which represents the freshness of the concerned events. Next, we formulate an optimization problem that minimizes the long-term AoII of remote state estimation through the devices deciding whether to sample the factory states at each time slot. To solve this problem, we first transform the original problem into a state-wise constrained Markov decision programming (CMDP) and then propose a soft actor-critic (SAC) based algorithm to learn a sampling policy to take sample actions within the sampling rate constraint, while considering packet error. Simulation results show that, the proposed algorithm can reduce the number of samples by up to 44% compared to the error-based sampling scheme, with the same estimation accuracy.

In this paper, the problem of semantic-based efficient image transmission is studied over the Internet of Vehicles (IoV). In the considered model, a vehicle shares massive amount of visual data perceived by its visual sensors to assist other vehicles in making driving decisions. However, it is hard to maintain a high reliable visual data transmission due to the limited spectrum resources. To tackle this problem, a semantic communication approach is introduced to reduce the transmission data amount while ensuring the semantic-level accuracy. Particularly, an image segmentation semantic communication (ISSC) system is proposed, which can extract the semantic features from the perceived images and transmit the features to the receiving vehicle that reconstructs the image segmentations. The ISSC system consists of an encoder and a decoder at the transmitter and the receiver, respectively. To accurately extract the image semantic features, the ISSC system encoder employs a Swin Transformer based multi-scale semantic feature extractor. Then, to resist the wireless noise and reconstruct the image segmentation, a semantic feature decoder and a reconstructor are designed at the receiver. Simulation results show that the proposed ISSC system can reconstruct the image segmentation accurately with a high compression ratio, and can achieve robust transmission performance against channel noise, especially at the low signal-to-noise ratio (SNR). In terms of mean Intersection over Union (mIoU), the ISSC system can achieve an increase by 75%, compared to the baselines using traditional coding methods.

Multi-source cross-project defect prediction (MSCPDP) refers to transferring defect knowledge from multiple source projects to the target project. MSCPDP has drawn increasing attention of academic and industry communities owing to its advantages compared with single-source cross-project defect prediction (SSCPDP) and some MSCPDP models have been proposed. However, to the best of our knowledge, there are no empirical studies to investigate the effect of different MSCPCP models on the performance of MSCPDP. To comprehensively investigate the performance of different MSCPDP models, we first conduct the literature research about MSCPDP studies, and then identify and compare 7 state-of-the-art MSCPDP models in terms of multiple performance measures including PD, PF, area under ROC curve (AUC), F1, precision, Matthews correlation coefficient (MCC), and Popt20% on 20 publicly available defect datasets. Furthermore, a robust multiple comparison method, i.e., the Scott-Knott effect-size difference (ESD) test, is used for statistical test. The experiment results show that 1) Burak’s Filter always performs best in terms of precision, AUC, MCC, Popt20% except for F1;2) MSCPDP models outperform the mean performance of SSCPDP models on most datasets; 3) the performance of MSCPDP models still needs to be further improved. We suggest software engineers use MSCPDP models but not SSCPDP models for CPDP and pay more attention to both the distribution difference of different datasets and the problems of sample similarity and weight when building MSCPDP models.

Context: Software defect number prediction (SDNP) helps allocate limited testing resources by ranking software modules according to the predicted defect numbers. However, the highly skewed distribution of defects greatly degrades the performance of SDNP models by preventing SDNP models from ranking software modules accurately. Objective: This paper introduces a novel subspace hybrid sampling ensemble (SHSE) method based on feature subspace construction, hybrid sampling , and ensemble learning for building high-performance SDNP models. Method: Specifically, we first construct a series of feature subspace to ensure the diversity of base learners. In each of feature subspace, we then use the proposed hybrid sampling method to balance the training subset without losing too much information and introducing lots of noisy data caused by only using undersampling or oversampling techniques. Finally, we train each base learner and combine them by using the proposed weighted ensemble strategy. Experiments are performed on 27 public defect datasets. We compare SHSE with five state-of-the-art resampling-based models and four zero-inflated/hurdle models in terms of the ranking performance measure fault-percentile-average (FPA). To demonstrate the effectiveness of SHSE, two statistical testing methods including Wilcoxon Signed-rank test and Scott–Knott Effect Size Difference test are utilized. Cliff’s δ is also computed for quantifying the difference when there is significant difference between SHSE and each baseline. Results: The experimental results show that SHSE significantly outperforms the baselines and improves the performance over each baseline with as least medium effect size on most datasets. On average, SHSE improves the performance over the resampling-based methods by 8.7% ∼ 14.4% and the zero-inflate/hurdle models by 10.3% ∼ 15.2%. Conclusion: It can be concluded that SHSE is a more promising alternative for software defect number prediction.

In this paper, the problem of audio based semantic communication is investigated over wireless networks. In the considered model, wireless edge devices must transmit large-sized audio data to a server using semantic communication techniques. The techniques enable the transmission of audio semantic information which captures the contextual features of audio signals. To extract the semantic information from audio signals, a wave to vector (wav2vec) architecture based autoencoder that consists of convolutional neural networks (CNNs) is proposed. The proposed autoencoder enables high-accuracy audio transmission with small amounts of data. To further improve the accuracy of semantic information extraction, federated learning (FL) is implemented over multiple devices and a server. Simulation results show that the proposed algorithm can converge effectively and can reduce the mean square error (MSE) between the recovered audio signals and the source audio signals by nearly 100 times, compared to a traditional coding scheme.

In this article, the problem of vertical handover in software-defined network (SDN) based heterogeneous networks (HetNets) is studied. In the studied model, HetNets are required to offer diverse services for mobile users. Using an SDN controller, HetNets have the capability of managing users' access and mobility issues but still have the problems of ping-pong effect and service interruption during vertical handover. To solve these problems, a mobility-aware seamless handover method based on multipath transmission control protocol (MPTCP) is proposed. The proposed handover method is executed in the controller of the software-defined HetNets (SDHetNets) and consists of three steps: location prediction, network selection, and handover execution. In particular, the method first predicts the user's location in the next moment with an echo state network (ESN). Given the predicted location, the SDHetNet controller can determine the candidate network set for the handover to pre-allocate network wireless resources. Second, the target network is selected through fuzzy analytic hierarchical process (FAHP) algorithm, jointly considering user preferences, service requirements, network attributes, and user mobility patterns. Then, seamless handover is realized through the proposed MPTCP-based handover mechanism. Simulations using real-world user trajectory data from Korea Advanced Institute of Science & Technology show that the proposed method can reduce the handover times by 10.85% to 29.12% compared with traditional methods. The proposed method also maintains at least one MPTCP subflow connected during the handover process and achieves a seamless handover.

Cross-project defect prediction (CPDP) refers to predicting defects in the target project lacking of defect data by using prediction models trained on the historical defect data of other projects (i.e., source data). However, CPDP requires the source and target projects have common metric set (CPDP-CM). Recently, heterogeneous defect prediction (HDP) has drawn the increasing attention, which predicts defects across projects having heterogeneous metric sets. However, building high-performance HDP methods remains a challenge owing to several serious challenges including class imbalance problem, nonlinear, and the distribution differences between source and target datasets. In this paper, we propose a novel kernel spectral embedding transfer ensemble (KSETE) approach for HDP. KSETE first addresses the class-imbalance problem of the source data and then tries to find the latent common feature space for the source and target datasets by combining kernel spectral embedding, transfer learning, and ensemble learning. Experiments are performed on 22 public projects in both HDP and CPDP-CM scenarios in terms of multiple well-known performance measures such as, AUC, G-Measure, and MCC. The experimental results show that (1) KSETE improves the performance over previous HDP methods by at least 22.7, 138.9, and 494.4 percent in terms of AUC, G-Measure, and MCC, respectively. (2) KSETE improves the performance over previous CPDP-CM methods by at least 4.5, 30.2, and 17.9 percent in AUC, G-Measure, and MCC, respectively. It can be concluded that the proposed KSETE is very effective in both the HDP scenario and the CPDP-CM scenario.