To address the challenges posed by exponential data growth, mobile edge computing (MEC) has emerged as a key solution by decentralizing server resources from the cloud to the network edge near mobile users, thereby facilitating high-quality and low-latency service delivery. However, failures often occur in real-world mobile networks, making it crucial to consider network robustness in addition to user coverage when deploying edge servers in an MEC network. In this paper, the Geographic space-oriented Search algorithm for Edge Server Placement (ESP-GS) is proposed to optimize both of these objectives. The core idea behind ESP-GS is to leverage a technique known as “Constraint Relaxation” to downscale the edge server placement from an m-dimensional combinatorial problem to a k-dimensional continuous problem in the geographic space. This approach significantly enhances scalability, making it well-suited for large-scale network deployments. Furthermore, since the robustness of mobile edge networks lacks a widely accepted metric, a Random Failure Evaluation (RFEval) method and two corresponding metrics are designed to assess failure tolerance in practical scenarios. Extensive comparative experiments have been conducted on real-world and publicly available datasets. The results show that the ESP-GS algorithm exhibits excellent performance in both user coverage and network robustness, improving the overall network performance by 8% to 27% compared to other benchmark algorithms.

Software developers can only obtain a very small amount of information from the individual failure-causing inputs, which makes debugging difficult. Therefore, it is necessary to explore additional failure-causing inputs (failure regions) using the known failure-causing inputs. In order to accurately and efficiently identify the failure region, we propose a novel two-stage search algorithm, TS-FRI. In the initial exploration stage, a round-robin search identifies several boundary failure-causing points, and the failure region's centroid is estimated. During the main search stage, the boundary failure-causing points are identified through iterative division of the input domain with an equally sized partitioning strategy. This results in the boundary points being as dispersed as possible around the failure-region boundary, with the polytope formed by the points approximating the failure region (e.g., a polygon in two dimensions). The proposed algorithm is validated through simulation and empirical analysis: The experimental results show that the TS-FRI accuracy is at least comparable to the best accuracy of the compared three algorithms, and can be ten times better. In addition, TS-FRI only takes a quarter of the computation time and half the failure-validation cost of the other algorithms.

Summary When selecting services from a cloud center to build applications, the quality of service (QoS) is an important nonfunctional attribute to be considered. However, in actual application scenarios, the QoS details for many services may not be available. This has led to a situation where prediction of the missing QoS records for services has become a key problem for service selection. This article presents a selective ensemble learning (SEL) framework for prefilling‐based matrix factorization (PFMF) predictors. In each PFMF predictor, the improved collaborative filtering is defined by examining the stability of the QoS records when measuring the similarity of users (or services), and then used to prefill empty records in the initial QoS matrix. To ensure the diversity of the basic PFMF predictors, various prefilled QoS matrices are constructed for the matrix factorization. In this process, different reference weights are assigned to the original and the prefilled QoS records. Finally, particle swarm optimization is used to set the ensemble weights for the basic PFMF predictors. The proposed SEL on PFMF (SEL‐PFMF) algorithm is validated on a public dataset, where its prediction performance outperforms the state‐of‐the‐art algorithms, and also shows good stability.

Abstract In recent years, deep learning (DL) systems are increasingly used in the safety‐critical fields such as autonomous driving, medical diagnosis, and financial service. Although these systems have demonstrated an outstanding performance in enhancing the accuracy of decision‐making, they pose significant challenges to the trustworthiness due to their limited interpretability and inherent uncertainty. Adaptive random testing (ART) has been proved as an effective approach for ensuring the reliability of DL systems. However, existing ART methods for DL systems incur a heavy overhead in test case selection due to the computation of distances. To address this issue, we propose a lightweight adaptive random testing (Lw‐ARTDL) method for DL systems. In our improved algorithm, we employ the K‐Means technique to divide the entire test suite into several subsets. Then, for a candidate test case, we only calculate distances between it and the test cases within the category to which it belongs. This partition strategy ensures that the selected test cases are more representative while significantly reducing the computational cost. To validate the proposed algorithm, the comparison experiments between Lw‐ARTDL and the original ARTDL algorithm are conducted on two typical DL systems. The experimental results show that Lw‐ARTDL significantly reduces the overhead of failure detection, and exhibits stronger failure detection capability compared to ARTDL in most similarity metrics.

Adaptive random testing (ART) improves the failure-detection effectiveness of Random testing (RT) by making test cases more evenly distributed in the input domain. The Fixed-size-Candidate-set ART (FSCS-ART) is one of the most classical algorithms, which selects the candidate test case furthest from the previously executed test case as the next test case. However, when the number of executed test cases is large, the computational overhead will be very high. In this paper, we propose an enhanced version of FSCS-ART based on a modified Metric-Memory tree (MM-tree), namely Fixed-size-Candidate-set ART based on the modified MM-tree (MMFC-ART). Simulations and empirical studies are conducted to verify the effectiveness and efficiency of MMFC-ART. The experimental results indicate that MMFC-ART significantly reduces the computational overhead while ensuring comparable or better failure-detection effectiveness than FSCS-ART. Meanwhile, compared with KD-tree-enhanced Fixed-size-Candidate-set ART (KDFC-ART), MMFC-ART has better performance in high dimensions in terms of efficiency. In terms of effectiveness, MMFC-ART has better failure-detection effectiveness in some scenarios. Overall, MMFC-ART is cost-effective compared to FSCS-ART and KDFC-ART.

With the rapid growth of 6G communication and smart sensor technology, the Internet of Things (IoT) has attracted much attention now. In the 6G‐based IoT applications on the multiprocessor platform, the partitioned scheduling has been widely applied. However, these partitioned scheduling approaches could cause system resource waste and uneven workload among processors. In this paper, a smart semipartitioned scheduling strategy (SSPS) was proposed for mixed‐criticality systems (MCS) in 6G‐based edge computing. Besides tasks’ acceptance rate and weighted schedulability, QoS is considered in SSPS to improve the service quality of the system. The SSPS allocates tasks into each processor, and some tasks can migrate to other processors as soon as possible. By comparing with the several existing algorithms, the experimental results show that the SSPS achieves the best in the schedulability and QoS of the system.

Diversity is the key factor for test cases to detect program failures. Adaptive random testing (ART) is one of the effective methods to improve the diversity of test cases. Being an ART algorithm, the evolutionary adaptive random testing (eAR) only increases the distance between test cases to enhance its failure detection ability. This paper presents a new ART algorithm, MoesART, based on multi-objective evolutionary search. In this algorithm, in addition to the dispersion diversity, two other new diversities (or optimization objectives) are designed from the perspectives of the balance and proportionality of test cases. Then, the Pareto optimal solution returned by the NSGA-II framework is used as the next test case. In the experiments, the typical block failure pattern in the cases of two-dimensional and three-dimensional input domains is used to validate the effectiveness of the proposed MoesART algorithm. The experimental results show that MoesART exhibits better failure detection ability than both eAR and the fixed-sized-candidate-set ART (FSCS-ART), especially for the programs with three-dimensional input domain.

Adaptive random testing (ART) achieves better failure‐detection effectiveness than random testing due to its even spreading of test cases. ART by random partitioning (RP‐ART) is a lightweight method, but its advantage over random testing is relatively low. Although iterative partition testing (IPT) method has good performance for detecting failures in a block pattern, it loses randomness during the test case generation. To overcome the shortcomings of the above two algorithms, a new algorithm named ART by flexible partitioning (FP‐ART) is proposed. In the FP‐ART, a set of random candidates is used to select an appropriate test case by considering their boundary distance. Accordingly, the corresponding sub‐domain is also partitioned by the new test case. Based on this kind of flexible partitioning, the randomness of test case selection can be guaranteed and the spatial distribution of test cases is even more diverse. According to the results in simulation and empirical experiments, FP‐ART demonstrates better failure‐detection effectiveness than RP‐ART and is more suitable to detect the failures in strip patterns than the IPT method. Meanwhile, its failure‐detection ability is much stronger than that of fixed‐size‐candidate‐set ART in the cases of a relatively high failure rate.

Adaptive random testing (ART) was developed as an enhanced version of random testing to increase the effectiveness of detecting failures in programs by spreading the test cases evenly over the input space. However, heavy computation may be incurred. In this paper, three enhanced algorithms for fixed-size-candidate-set ART (FSCS-ART) are proposed based on the k-dimensional tree (KD-tree) structure. The first algorithm Naive-KDFC constructs a KD-tree by splitting the input space with respect to every dimension successively in a round-robin fashion. The second algorithm SemiBal-KDFC improves the balance of the KD-tree by prioritizing the splitting according to the spread in each dimension. In order to control the number of traversed nodes in backtracking, the third algorithm LimBal-KDFC introduces an upper bound for the nodes involved. Simulation and empirical studies have been conducted to investigate the efficiency and effectiveness of the three algorithms. The experimental results show that these algorithms significantly reduce the computation time of the original FSCS-ART for low dimensions and for the case of high dimensions with low failure rates. The efficiency of SemiBal-KDFC is better than that of Naive-KDFC when the dimension is no more than 8, but LimBal-KDFC is the most efficient of all three. Although the limited backtracking leads only to an approximate nearest neighbor in LimBal-KDFC, its failure-detection effectiveness is, in fact, better than FSCS-ART in high-dimensional input spaces and has no significant deterioration in low-dimensional spaces.

Bisection-based adaptive random testing (B-ART) is a lightweight method for test case generation. However, its failure detection effectiveness is not so ideal. In this paper, two strategies are designed to overcome the disadvantage as above. The first one is the flexible partitioning strategy, in which the splitting line (or plane) is determined according to the relative position of the test case within the region to be bisected. Secondly, given an empty sub-region, candidate strategy is applied to select an appropriate candidate whose boundary distance is the largest in the set of random candidates as the next test case. Based on these two strategies, an improved algorithm named B-ART-FPCS is proposed. To verify the effectiveness of B-ART-FPCS algorithm, simulation analysis is performed for the comparison between the original B-ART and B-ART-FPCS. The experimental results show that B-ART-FPCS exhibits the stronger failure detection capability than B-ART for block failure pattern and most cases of point pattern. In addition, the linear-order time complexity of B-ART-FPCS is analyzed in theory and confirmed by experiments.

The quality of test data has an important impact on the effect of software testing, so test data generation has always been a key task for finding the potential faults in program code. In structural testing, the primary goal is to cover some kinds of structure elements with some specific inputs. Search-based test data generation provides a rational way to handle this difficult problem. In the past, some well-known meta-heuristic search algorithms have been successfully utilized to solve this issue. In this paper, we introduce a variant of genetic algorithm (GA), called quantum-inspired genetic algorithm (QIGA), to generate the test data with stronger coverage ability. In this new algorithm, the traditional binary bit is replaced by a quantum bit (Q-bit) to enlarge the search space so as to avoid falling into local optimal solution. On the other hand, some other strategies such as quantum rotation gate and catastrophe operation are also used to improve algorithm efficiency and quality of test data. In addition, experimental analysis on eight real-world programs is performed to validate the effectiveness of our method. The results show that QIGA-based method can generate test data with higher coverage in much smaller convergence generations than GA-based method. More importantly, our proposed method is more robust for algorithm parameter change.

Web service is the emergent technology for constructing more complex and flexible software system for business applications. However, some new features of Web service-based software such as heterogeneity and loose coupling bring great trouble to the latter fault debugging and diagnosis. In the paper, variable precision rough set-based diagnosis framework is presented. In such debugging model, SOAP message monitoring and service invocation instrument are used to record service interface information. Meanwhile, factors of execution context are also viewed as conditional attributes of knowledge representation system. The final execution result is treated as the decision attribute, and failure ontology is utilized to classify system's failure behaviors. Based on this extended information system, variable precision rough set reasoning is performed to generate the probability association rules, which are the clues for locating the possible faulty services. In addition, the experiment on a real-world Web services system is performed to demonstrate the feasibility and effectiveness of our proposed method.

Web services technology provides a flexible and cost-effective paradigm to construct highly dynamic systems through service discovery, composition, and ultra-late binding. However, its new features bring great pressure to maintain Web service-based system. Based on the massive testing results, how to locate the fault points in system is a challenging task. In the paper, a two level diagnosis framework for Web services system is proposed. In service unit level, the WSDL interface information is used to construct decision table. In service composition level, the decision information system is built by comprehensively using process specifications and interface information. Then, rule mining algorithm in rough set reasoning is adopted to reveal the input cases associated with service or system failures. How to utilize such rules to locate faults in Web services system is also discussed. In addition, two cases are introduced to validate the feasibility and effectiveness of our approach.

Network representation is a convenient and intuitive abstraction for analyzing the massive interacting data. Some topological characteristics of the network have been found in the past decade, and community structure is the typical one of them. Community detection has become a hot topic in complex network analysis. In the paper, a hybrid algorithm is presented for solving such problem. At first, we take the max-degree node as the "core" node. Then, the diffusing operation is per-formed on it to produce two preliminary partitions. Subsequently, an EO-based adjustment step is used for generating the final partitioning with high quality. In addition, four real-world networks are used for validating the efficiency and effectiveness of our hybrid algorithm. The experimental results show that the proposed hybrid algorithm is a promising solution for solving the community detection problem both in precision and efficiency.

With the prevalence of Web applications, its linkage structures become more and more complex and the whole architecture of a Web site has always been experiencing evolvement. Therefore, analyzing and visualizing the linkage structure of a Web application is quite beneficial to the building of a new Web application or the updating of an old one. The paper proposes a visualization and dependency analysis framework for a Web application, and implements a prototype tool WASViewer. Based on the deep analysis for the text feature of hyperlink, a regular expression-based linkage information extraction method is presented. Then, a third-part component WinGraphviz, is adopted to visualize the dependency relation, which is expressed via adjacent matrix. The complexity metrics for linkage structure graph are also addressed. We found that the degree distribution of dependency graph roughly obeys the power law. In addition, a medium-size Web application is used to validate the effectiveness of our methods.

Web services can be combined in a collaborative way to create a new Web services system (WSS) to solve more complex problems. However, WSS testing is not a trivial task, features like distribution, loose-coupling, and collaboration bring great pressure to the latter testing activity. In the paper, a hierarchical testing and evaluation strategy is proposed from the perspective of the developers of Web services system. In service unit level, combinatorial testing method is used to ensure single service's quality. In system level, BPEL specification is converted into state diagram firstly, and then state transition-based test cases generation algorithm is presented. Based on the testing records of the above two levels, a simple reliability evaluation method is addressed. Furthermore, the feasibility and effectiveness of our approach is validated by the preliminary study and case-based experiments.

Software testing is a practical activity combined with theory, technology, tool, and management. Assistant tool for testing plays an important role in software development in practice, and should not be neglected. Taking the popularly used C and C+ + programs as the test objects, this paper explores how to implement a semi-automatic tool (named CppTest) with the testing capability in three levels, namely (1) structural testing in method level, (2) state-based class level testing through modeling state transition behaviors using an extended finite state machine (EFSM), and (3) system level black-box testing with some traditional strategies. During the later debugging stage, the prototype system can perform clustering analysis on the failure executions and sample fairly few representative test executions to direct program diagnosis. CppTest mainly possesses the merits as follows: (1) multi-levels' testing support, (2) high automation and visualization, (3) test project management, and (A) favorable scalability. In addition, some future research directions are also explored

High evolvability is the remarkable character of component-based software (CBS), and brings great pressure to the testing activity. Recently, aspect-oriented programming has been proposed as an effective technique for modulating separate concerns, and facilitating the maintenance and evolution of software system. In this paper, we use this technique to improve component's testability so as to facilitate component's unit testing and regression testing of CBS as follows: Self-checking aspect is embedded to check the invariants which the component should obey, and tracing aspect is introduced to collect precondition of method execution in component so as to help regression testers to pick out precise subset of test suit. In addition, two examples are used to demonstrate the feasibility and effectiveness of our presented methods.

Some specialties of component, such as high evolvability, implementation transparent, and limited access support, bring a great challenge for testing the systems built by externally-provided components, especially for regression testing. Built-in test design is a fairly effective way to improve component's testability. In this paper, we present a built-in regression testing method to validate the change and its impact of component-based software, which needs the mutual collaboration between the component developers and component users. Through employing preliminary experiments on some medium scale systems, our regression testing method based on built-in test design has been proven to be feasible and practical. Although our method indicates the same precision as Orso et al. 's method at statement level, it needs less exchanged information (i.e., meta-data) and test scripts, so it is more cost-effective.

Component-based software (CBS) is built through the ways of composition and integration, this development style is quite favorable for modification or upgrade of system. However, making sure that the modified system still works is a challenge. In order to facilitate the maintenance activities and their optimum configuration, it is necessary to measure the changes. This paper mainly discusses the case of component modification, including the single component change and the changes of multi-components, and presents the algorithms for measuring the change impacts in CBS for each situation. Finally, the on-going research plan is addressed.

In recent years, component-based software has been widely used in various application domains and becomes a fairly popular software form. However, due to the lack of information about the externally-developed components, system testers (i.e., component users) generally can't perform effective testing (especially regression testing) on their component-based systems. Component users don't know the details about change in component, so they aren't able to select the proper test cases to retest the modified system. In this paper, we present an improved regression testing method based on the enhanced change information of component version to test the software system containing some modified components. It is a collaborative testing method, needing the joint participations of component developer and user. Component developers calculate the change information from labeled method call graph and provide it to component users via XML files. Component users use this change information and their instrumentation records together to pick out test cases for next-round testing. In addition, we have employed preliminary experiments on some medium scale systems, the experiment results show that our regression testing method is fairly feasible and cost-effective in practice.

Exception handling is a powerful mechanism that separates the error handling code from normal code. It makes software do its utmost to run on the normal state. However, incorrect usage of exception will bring about more potential faults in the code. Based on the study of exception model of C++, a typical object-oriented language, we present a method of constructing control flows for both explicit exception and implicit exception. Subsequently, provide a model that can improve the robustness of programs via static exception analysis. This paper also discusses methods of dynamic exception structural testing and proposes several testing strategies. Finally, a rough prototype tool for exception analysis and testing has been implemented, and some preliminary experiments have been performed under it. The experiment results show that our methods of static exception inspection and dynamic exception testing are fairly effective to expose the potential exception errors. Furthermore, statistical structural exception testing is also considerably useful to find out some normal faults.