Big data applications are currently used in many application domains, ranging from statistical applications to prediction systems and smart cities. However, the quality of these applications is far from perfect, such as functional error, failure and low performance. Consequently, assuring the overall quality for big data applications plays an increasingly important role. This paper aims at summarizing and assessing existing quality assurance (QA) technologies addressing quality issues in big data applications. We have conducted a systematic literature review (SLR) by searching major scientific databases, resulting in 83 primary and relevant studies on QA technologies for big data applications. The SLR results reveal the following main findings: (1) the quality attributes that are focused for the quality of big data applications, including correctness, performance, availability, scalability and reliability, and the factors influencing them; (2) the existing implementation-specific QA technologies, including specification, architectural choice and fault tolerance, and the process-specific QA technologies, including analysis, verification, testing, monitoring and fault and failure prediction; (3) existing strengths and limitations of each kind of QA technology; (4) the existing empirical evidence of each QA technology. This study provides a solid foundation for research on QA technologies of big data applications and can help developers of big data applications apply suitable QA technologies.

With the rapid development of blockchain technology, smart contract applications have become increasingly widespread. However, vulnerabilities in contracts may be exploited by attackers, causing serious financial losses. In recent years, learning-based approaches have gained prominence for their accuracy and efficiency by automatically extracting explicit syntactic or semantic features from a large number of smart contracts with minimal manual intervention. In this article, we conduct a comprehensive analysis and ultimately select 61 scientific publications to provide researchers, especially beginners, with a comprehensive understanding of the learning-based detection process and guidance on selecting appropriate code representations. We first introduce common types of vulnerabilities, detail uncovered vulnerabilities, and summarize datasets used in learning-based methods. Then, we elaborate on the general process of learning-based detection and classify existing publications based on code representations, including sequence, tree, graph, and mixed features. Finally, we summarize the progress of existing work and explore future research directions in this field.

This paper presents a novel probabilistic Quality of Service (QoS) monitoring method named DLSTM-BRPM (Double Long Short Term Memory (DouLSTM-Den) based Bayesian Runtime Proactive Monitoring) to accurately and efficiently monitor QoS in a mobile edge environment. This method consists of a DouLSTM-Den model and a Gaussian Hidden Bayesian classifier. The DouLSTM-Den model aims to predict a user’s future movement trajectory in real time and proactively monitor the spatio-temporal QoS performance of services based on the predicted trajectory. The Gaussian Hidden Bayesian classifier is employed to accurately monitor QoS by constructing parent attributes to reduce the interdependence between QoS attributes. Our experiments based on public synthetic datasets demonstrate the effectiveness of the proposed method over state-of-the-art solutions. We also conducted experiments in a real-world edge environment to validate the feasibility of the proposed method.

Composite services evolve for various reasons. Test case selection in the regression testing is an effective technique to ensure the correctness of modified versions meanwhile to reduce the cost of testing. However, few work has studied the test case selection problem based on the data flow testing criteria. In addition, there are three observable kinds of changes during the evolution, including Process change, Binding change and Interface change, which all bring impact to the data flow. To address these issues, a test case selection approach is proposed for regression testing of BPEL (Business Process Execution Language) composite service where all-uses criterion is satisfied and all the three change types are involved. BPEL composite service is modeled with a two-level model in which XCFG (eXtended Control Flow Graph) describes the behavior of BPEL process in the first level and WSDM (Web Service Description Model) depicts the interface information of composite service and partner services in the second level. Change impact analysis is performed to identify the affected definition-use pairs by comparing and analyzing two-level models of the baseline and evolved versions. And testing paths are generated to cover the affected definition-use pairs and select test cases based on the path condition analysis. Empirical result shows that the proposed approach is effective.

Deep learning (DL) components have been broadly applied in diverse applications. Similar to traditional software engineering, effective test case generation methods are needed by industry to enhance the quality and robustness of these deep learning components. To this end, we propose a novel automatic software testing technique, TAEFuzz (Automatic Fuzz -Testing via T ransferable A dversarial E xamples), which aims to automatically assess and enhance the robustness of image-based deep learning (DL) systems based on test cases generated by transferable adversarial examples. TAEFuzz alleviates the over-fitting problem during optimized test case generation and prevents test cases from prematurely falling into local optima. In addition, TAEFuzz enhances the visual quality of test cases through constraining perturbations inserted into sensitive areas of the images. For a system with low robustness, TAEFuzz trains a low-cost denoising module to reduce the impact of perturbations in transferable adversarial examples on the system. Experimental results demonstrate that the test cases generated by TAEFuzz can discover up to 46.1% more errors in the targeted systems, and ensure the visual quality of test cases. Compared to existing techniques, TAEFuzz also enhances the robustness of the target systems against transferable adversarial examples with the perturbation denoising module.