IET Software最新文献

Covering array generation (CAG) is the key research problem in combinatorial testing and is an NP-complete problem. With the increasing complexity of software under test and the need for higher interaction covering strength t, the techniques for constructing high-strength covering arrays are expected. This paper presents a hybrid heuristic memetic algorithm named QSSMA for high-strength CAG problem. The sub-optimal solution acceptance rate is introduced to generate multiple test cases after each iteration to improve the efficiency of constructing high-covering strength test suites. The QSSMA method could successfully build high-strength test suites for some instances where t up to 15 within one day cutoff time and report five new best test suite size records. Extensive experiments demonstrate that QSSMA is a competitive method compared to state-of-the-art methods.

Corona Virus disease 2019 (COVID-19) has shattered people's daily lives and is spreading rapidly across the globe. Existing non-pharmaceutical intervention solutions often require timely and precise selection of small areas of people for containment or even isolation. Although such containment has been successful in stopping or mitigating the spread of COVID-19 in some countries, it has been criticized as inefficient or ineffective, because of the time-delayed and sophisticated nature of the statistics on determining cases. To address these concerns, we propose a GSA-ELM model based on a gravitational search algorithm to forecast the global number of active cases of COVID-19. The model employs the gravitational search algorithm, which utilises the gravitational law between two particles to guide the motion of each particle to optimise the search for the global optimal solution, and utilises an extreme learning machine to address the effects of nonlinearity in the number of active cases. Extensive experiments are conducted on the statistical COVID-19 dataset from Johns Hopkins University, the MAPE of the authors’ model is 7.79%, which corroborates the superiority of the model to state-of-the-art methods.

Cyber Physical Systems (CPS) are becoming more ubiquitous, complex and powerful as well as more and more present in our daily life. The inherent benefit and comfort come with an environmental impact at every step of their life-cycle. This impact is significant and unfortunately often ignored today. As cyber-physical systems tend to be ‘invisible’, there is a need for awareness of the underlying infrastructure and required resources, early in the design phases. In this article, the environmental impact considerations in the early stages of the implementation and opportunities to improve design choices with a people-planet-system perspective are discussed. The authors discuss the aspects related to system configuration, data management and the overall goal and functionalities supported by the CPS. Through a specific smart home case, the potential of considering life-cycle assessment of both the devices and data management is illustrated. By explicitly considering different configurations, it will be possible to analyse the environmental impacts of the design decisions. Our research in progress targets a design approach to converge into an equilibrium between utility, performance, and minor environmental impact of smart systems.

Recent developments in the field of Internet of things (IoT) have aroused growing attention to the security of smart devices. Specifically, there is an increasing number of malicious software (Malware) on IoT systems. Nowadays, researchers have made many efforts concerning supervised machine learning methods to identify malicious attacks. High-quality labels are of great importance for supervised machine learning, but noises widely exist due to the non-deterministic production environment. Therefore, learning from noisy labels is significant for machine learning-enabled Malware identification. In this study, motivated by the symmetric cross entropy with satisfactory noise robustness, the authors propose a robust Malware identification method using temporal convolutional network (TCN). Moreover, word embedding techniques are generally utilised to understand the contextual relationship between the input operation code (opcode) and application programming interface function names. Here, considering the numerous unlabelled samples in real-world intelligent environments, the authors pre-train the TCN model on an unlabelled set using a word embedding method, that is, Word2Vec. In the experiments, the proposed method is compared with several traditional statistical methods and more recent neural networks on a synthetic Malware dataset and a real-world dataset. The performance comparisons demonstrate the better performance and noise robustness of their proposed method, especially that the proposed method can yield the best identification accuracy of 98.75% in real-world scenarios.

Requirements engineering is a fundamental process in software development phases. At the same time, it is a difficult phase and exposed many ethical violations. The main purpose is proposing an ethical framework for software requirements engineering that addresses the identified concerns. These concerns include problems associated with a knowledge gap, requirements identification, quality-related concerns, unwillingness to give requirements, and practicing forbidden activities. These concerns are grouped into a category as the proposed framework components. Each of the categories encompasses more than one problem domain. The proposed framework suggests resolving mechanisms as collections of clauses for each of those concerns. An expert evaluation technique is used to validate the proposed framework. The experts are purposefully selected from software industries and institutions. Questionnaires and focus group discussions were used as data-gathering tools for the validation of the proposed framework. The validity (face validity, content validity, and construct validity) and the reliability of the proposed framework were checked. The evaluation results show that the proposed framework has an acceptable range of validity and reliability. The proposed framework can be used as a guideline for software engineers to minimise the occurrence of those identified concerns during the requirements engineering process.

Effort-aware cross-project defect prediction (EACPDP), which uses cross-project software modules to build a model to rank within-project software modules based on the defect density, has been suggested to allocate limited testing resource efficiently. Recently, Ni et al. proposed an EACPDP method called EASC, which used all cross-project modules to train a model without considering the data distribution difference between cross-project and within-project data. In addition, Ni et al. employed the different defect density calculation strategies when comparing EASC and baseline methods. To explore the effective defect density calculation strategies and methods on EACPDP, the authors compare four data filtering methods and five transfer learning methods with EASC using four commonly used defect density calculation strategies. The authors use three classification evaluation metrics and seven effort-aware metrics to assess the performance of methods on 11 PROMISE datasets comprehensively. The results show that (1) The classification before sorting (CBS+) defect density calculation strategy achieves the best overall performance. (2) Using balanced distribution adaption (BDA) and joint distribution adaptation (JDA) with the K-nearest neighbour classifier to build the EACPDP model can find 15% and 14.3% more defective modules and 11.6% and 8.9% more defects while achieving the acceptable initial false alarms (IFA). (3) Better comprehensive classification performance of the methods can bring better EACPDP performance to some extent. (4) A flexible adjustment of the defect threshold λ of the CBS+ strategy contribute to different goals. In summary, the authors recommend researchers and practitioners use to BDA and JDA with the CBS+ strategy to build the EACPDP model.

Formal methods are proven techniques that provide a rigorous mathematical basis to software development. In particular, they allow the quality of development to be effectively improved by making accurate and explicit modelling, so that anomalies like ambiguities and incompleteness are identified in the early phases of the software development process. Semi-formal UML models and formal Timed Automata models are used to design a telerehabilitation system through a practical approach based on abstraction and refinement. The formal verification of expected properties of the system is performed by the Uppaal tool. The motivation of this work is threefold: (i) showing the usefulness of formal methods to satisfy the validation needs of a medical telerehabilitation system; (ii) demonstrating our approach of system analysis through refinements to guide the development of a complex system; and (iii) highlighting, from a real-life experience, the usefulness of models to involve the stakeholders all along the design of a system, from requirements to detailed specifications.

Just-In-Time (JIT) defect prediction aims to predict the defect proneness of software changes when they are initially submitted. It has become a hot topic in software defect prediction due to its timely manner and traceability. Researchers have proposed many JIT defect prediction approaches. However, these approaches cannot effectively utilise line labels representing added or removed lines and ignore the noise caused by defect-irrelevant files. Therefore, a JIT defect prediction model enhanced by the joint method of line label Fusion and file Filtering (JIT-FF) is proposed. Firstly, to distinguish added and removed lines while preserving the original software changes information, the authors represent the code changes as original, added, and removed codes according to line labels. Secondly, to obtain semantics-enhanced code representation, a cross-attention-based line label fusion method to perform complementary feature enhancement is proposed. Thirdly, to generate code changes containing fewer defect-irrelevant files, the authors formalise the file filtering as a sequential decision problem and propose a reinforcement learning-based file filtering method. Finally, based on generated code changes, CodeBERT-based commit representation and multi-layer perceptron-based defect prediction are performed to identify the defective software changes. The experiments demonstrate that JIT-FF can predict defective software changes more effectively.

Official documentation of software development technologies, for example, APIs, may not be sufficient for all developer needs, so searching on the Internet is a usual practice. Nonetheless, finding useful information may be challenging because the best solutions are not always among the first ranked pages. Developers need to read and discard irrelevant pages, that is, those without code examples or those that have content with little focus on the desired solution. This work aims at proposing an approach to mine relevant solutions for programming tasks from search engine results by removing irrelevant pages. The authors evaluated the top-20 pages returned by the Google search engine, for 10 different queries, and observed that only 31% of the evaluated pages are relevant to developers. Then, the authors proposed and evaluated three different approaches to mine the relevant pages returned by the search engine. Google's search engine has been used as a baseline, and authors’ results have shown that it returns a reasonable number of irrelevant pages for developers, and the authors could establish an effective approach to remove irrelevant pages, suggesting that developers could benefit from a customised web search filter for development content.