Background : Code-line-level bugginess identification (CLBI) is an important area within software quality assurance, aiming to pinpoint potential buggy source code lines in a given software product. Recently, two concurrent approaches, GLANCE and DeepLineDP, have showcased impressive performance by respectively leveraging syntactic and semantic features compared with the existing state-of-the-art (SOTA) approaches in this field. Problem : Yet, the literature lacks a thorough investigation that fuses these two types of features to enhance CLBI. Such fusion holds the promise of significantly improving the efficacy of identifying defective lines. Objective : We aim to advance CLBI by fusing syntactic and semantic features, thereby harnessing their respective strengths. Method : We propose to build a CLBI approach, booSting DeePLineDP wIth syntaCtic fEatures (SPLICE) , by fusing syntactic features from GLANCE and semantic features from DeepLineDP. SPLICE comprises three variants—SPLICE-S, SPLICE-G, and SPLICE-F—each utilizing a unique line-level sorting approach. We make a comprehensive comparison with the existing SOTA approaches using six performance metrics. Result : Through an analysis of nine open source projects, our experimental results reveal that SPLICE is competitive with current SOTA CLBI approaches. Notably, SPLICE-F demonstrates superiority over all SOTA CLBI approaches, including GLANCE and DeepLineDP, across all six metrics, indicating a substantial improvement. Conclusion : This discovery underscores the critical importance of future CLBI research in fusing syntactic and semantic features to construct more effective bugginess identification approaches. It is worth noting that the analysis was conducted within the context of Java programs, which highlights the potential for exploring similar methods in other programming languages in future research.

A lightweight two-tier key authentication protocol based on blockchain is proposed to solve the edge server security and user privacy problems in Mobile Edge Computing (MEC). The protocol is based on blockchain technology and adopts a combination of elliptic curve cryptography and HMAC technology to deal with the authentication and key negotiation problems between the user and the edge. Considering the internal attack problem of the edge server, the key storage and computation in the protocol are transparent to the edge server and are done in the blockchain, and the efficient and secure management of the key is achieved through smart contracts. Experimental analysis shows that the protocol meets the basic security requirements, enhances the security of edge computing, and also outperforms other existing schemes in terms of efficiency. Therefore, it provides an efficient and feasible solution to the authentication problem in the MEC environment.

Combining discrete difference evolution and convex optimization, a privacy protection task offloading algorithm is proposed for solving the new location privacy leakage problem at mobile terminals due to offloading tasks to multiple edge servers for computation. First, according to the characteristics of location privacy problem, the privacy cost of offloading decision is defined. Second, to minimize the total cost of task offloading, the objective problem is modeled as a joint optimization of offloading decision and transmission power allocation. Finally, to solve the problem efficiently, offloading decision variable is decoupled from transmission power variable; a discrete difference evolutionary algorithm is designed to accumulate the approximate optimal offloading decision, and the non-convex optimization problem is transformed into a convex optimization problem through variable substitution, to solve the optimal transmission power efficiently. Simulation results show the proposed algorithm has good convergence and real-time performance and can reduce the total cost of task offloading effectively.