Software is designed for humans and must account for their values. However, current research and practice focus on a narrow range of well-explored values, e.g. security, overlooking a more comprehensive perspective. Those exploring a broader array of values rely on manual identification, which is labour-intensive and prone to human bias. Moreover, existing methods offer limited reliability as they fail to explain their findings. In this paper, we propose leveraging the reasoning capabilities of Large Language Models (LLMs) for automated inference about values. This allows for not only detecting values but also explaining how they are expressed in the software. We aim to examine the effectiveness of LLMs, specifically ChatGPT (Chat Generative Pre-Trained Transformer), in automated detection and explanation of values in software artifacts. Using ChatGPT, we investigate how mobile APIs align with human values based on their documentation. Human evaluation of ChatGPT's findings shows a reciprocal shift in understanding values, with both ChatGPT and experts adjusting their assessments through dialogue. While experts recognise ChatGPT's potential for revealing values, emphasis is placed on human involvement to enhance the accuracy of the findings by detecting and eliminating convincing but inaccurate explanations provided by the language model due to potential hallucinations or confabulations.

Deep neural network (DNN)-based vehicle detection systems deployed on unmanned aerial vehicles (UAVs) are susceptible to adversarial attacks, resulting in significant implications for public safety and system reliability. Despite advancements in DNN-based detection, the adversarial robustness of these systems in aerial video contexts remains underexplored. Existing attack models fail to exploit the sequential and periodic nature of video frames in aerial vehicle detection systems. To address this, we propose a Periodic Adversarial Attack for Aerial Video (P3AV), which is the first to take advantage of the periodic nature of tasks related to road traffic parameters and improve the success of attacks. P3AV systematically selects critical video frames to be attacked by employing Bayesian optimization combined with domain-specific knowledge. The sensitive pixels in the frames are then chosen based on the gradient magnitudes of the loss function. Finally, an improved version of the projected gradient descent algorithm is developed by using gradient norms to generate perturbations and enhance the manipulation of selected pixels. Our experiments using four adversarial attacks against 10 DNN architectures, which are developed based on Convolutional Neural Network (CNN) and YOLO, on two datasets demonstrate that P3AV can improve the false rate in detection systems by 6% and the attack success rate by 5% over other attack models. Meanwhile, CNN models perform the worst against adversarial attacks. These findings highlight the critical need for improved adversarial defenses in UAV-based detection systems and underscore the broader implications for secure and reliable ITS.

Malware injection attacks are among the most sophisticated and elusive threats in cybersecurity, characterised by their capacity for privilege escalation, obfuscation, and the ability to deceive antivirus software. This paper introduces a multi-layer architecture, featuring innovative deep neural networks, fast Fourier convolution , and association rule mining strategies, designed for the early detection and defusal of malware injection attacks. We then propose a proactive AI-enabled malware detection platform, DeepRadar , as a novel real-world defence mechanism. This early warning functionality capable of anticipating the attack a few cycles before occurrence represents a novel idea and unique approach to detecting malware injection attacks. The experimental results validate DeepRadar’s superior performance compared to not only previous related studies but also a standard benchmark of well-reputed antivirus applications under various scenarios and accredited datasets, including heavily obfuscated emerging malware variants and adversarial samples. It demonstrates higher Accuracy, F-score, ROC, and AUC metrics in early detection and classification of malware injection attacks while DeepRadar consumes significantly fewer system resources, including processor and memory during long-term scalable operation. The proposed early warning system succeeded in repelling up to 97.2% of attacks before malware could complete their malicious sequence. Lastly, the evaluation results were substantiated by formal statistical analysis using Friedman and Wilcoxon tests. The findings of this research and DeepRadar’s runtime scanner provide vital early warnings against stealthy malware and injection attacks, offering robust protection for sensitive systems and critical infrastructure.

Artificial Intelligence has demonstrated its significance in software engineering through notable improvements in productivity, accuracy, collaboration, and learning outcomes.This study examines the impact of generative AI tools, specifically ChatGPT, on the software development experiences of undergraduate students. Over a three-month project with seven students, ChatGPT was used as a support tool. The research focused on assessing ChatGPT’s effectiveness, benefits, limitations, and its influence on learning. Results showed that ChatGPT significantly addresses skill gaps in software development education, enhancing efficiency, accuracy, and collaboration. It also improved participants’ fundamental understanding and soft skills. The study highlights the importance of incorporating AI tools like ChatGPT in education to bridge skill gaps and increase productivity, but stresses the need for a balanced approach to technology use. Future research should focus on optimizing ChatGPT’s appli cation in various development contexts to maximize learning and address specific challenges.

Abstract Context Software practitioners adopt approaches like DevOps, Scrum, and Waterfall for high-quality software development. However, limited research has been conducted on exploring software development approaches concerning practitioners’ discussions on Q &A forums. Objective We conducted an empirical study to analyze developers’ discussions on Q &A forums to gain insights into software development approaches in practice. Method We analyzed 13,903 developers’ posts across Stack Overflow (SO), Software Engineering Stack Exchange (SESE), and Project Management Stack Exchange (PMSE) forums. A mixed method approach, consisting of the topic modeling technique (i.e., Latent Dirichlet Allocation (LDA)) and qualitative analysis, is used to identify frequently discussed topics of software development approaches, trends (popular, difficult topics), and the challenges faced by practitioners in adopting different software development approaches. Findings We identified 15 frequently mentioned software development approaches topics on Q &A sites and observed an increase in trends for the top-3 most difficult topics requiring more attention. Finally, our study identified 49 challenges faced by practitioners while deploying various software development approaches, and we subsequently created a thematic map to represent these findings. Conclusions The study findings serve as a useful resource for practitioners to overcome challenges, stay informed about current trends, and ultimately improve the quality of software products they develop.

Architecting software-intensive systems can be a complex process. It deals with the daunting tasks of unifying stakeholders’ perspectives, designers’ intellect, tool-based automation, pattern-driven reuse, and so on, to sketch a blueprint that guides software implementation and evaluation. Despite its benefits, architecture-centric software engineering (ACSE) suffers from a multitude of challenges. ACSE challenges could stem from a lack of standardized processes, socio-technical limitations, and scarcity of human expertise etc. that can impede the development of existing and emergent classes of software. Software Development Bots (DevBots) trained on large language models can help synergise architects’ knowledge with artificially intelligent decision support to enable rapid architecting in a human-bot collaborative ACSE. An emerging solution to enable this collaboration is ChatGPT, a disruptive technology not primarily introduced for software engineering, but is capable of articulating and refining architectural artifacts based on natural language processing. We detail a case study that involves collaboration between a novice software architect and ChatGPT to architect a service-based software. Future research focuses on harnessing empirical evidence about architects’ productivity and explores socio-technical aspects of architecting with ChatGPT to tackle challenges of ACSE.

Quantum computing systems rely on the principles of quantum mechanics to perform a multitude of computationally challenging tasks more efficiently than their classical counterparts. The architecture of software-intensive systems can empower architects who can leverage architecture-centric processes, practices, description languages to model, develop, and evolve quantum computing software (quantum software for short) at higher abstraction levels. We conducted a Systematic Literature Review (SLR) to investigate (i) architectural process, (ii) modelling notations, (iii) architecture design patterns, (iv) tool support, and (iv) challenging factors for quantum software architecture. Results of the SLR indicate that quantum software represents a new genre of software-intensive systems; however, existing processes and notations can be tailored to derive the architecting activities and develop modelling languages for quantum software. Quantum bits (Qubits) mapped to Quantum gates (Qugates) can be represented as architectural components and connectors that implement quantum software. Tool-chains can incorporate reusable knowledge and human roles (e.g., quantum domain engineers, quantum code developers) to automate and customise the architectural process. Results of this SLR can facilitate researchers and practitioners to develop new hypotheses to be tested, derive reference architectures, and leverage architecture-centric principles and practices to engineer emerging and next generations of quantum software.

Artificial intelligence (AI) solutions and technologies are being increasingly adopted in smart systems contexts; however, such technologies are concerned with ethical uncertainties. Various guidelines, principles, and regulatory frameworks are designed to ensure that AI technologies adhere to ethical well-being. However, the implications of AI ethics principles and guidelines are still being debated. To further explore the significance of AI ethics principles and relevant challenges, we conducted a survey of 99 randomly selected representative AI practitioners and lawmakers (e.g., AI engineers and lawyers) from 20 countries across five continents. To the best of our knowledge, this is the first empirical study that unveils the perceptions of two different types of population (AI practitioners and lawmakers) and the study findings confirm that transparency, accountability, and privacy are the most critical AI ethics principles. On the other hand, lack of ethical knowledge, no legal frameworks, and lacking monitoring bodies are found to be the most common AI ethics challenges. The impact analysis of the challenges across principles reveals that conflict in practice is a highly severe challenge. Moreover, the perceptions of practitioners and lawmakers are statistically correlated with significant differences for particular principles (e.g. fairness and freedom) and challenges (e.g. lacking monitoring bodies and machine distortion). Our findings stimulate further research, particularly empowering existing capability maturity models to support ethics-aware AI systems’ development and quality assessment.

Nowadays, vehicle-oriented Internet of Things (IoT) is a new generation of IoT networks in which sensors are deployed on electronic hardware modules of vehicles. A secure and feasible IoT-assisted vehicle environment should include a robust data transmission mechanism for transferring and collecting data packets from both onboard and roadside sensors, resulting in the accurate delivery of packages without delay. When designing such Internet of Vehicles (IoV) networks, the vulnerability of the network should be considered to facilitate data transmission in the remaining network under the condition that some nodes (e.g., vehicles) and channels are damaged due to the dynamic environmental factors and unpredicted failures at various nodes. Fractional Critical Deleted Graph (FCDG), which is used in graph theory, can act as Fractional Factor (FF) in the IoV networks to maintain the IoT network stable and provide reliable network connectivity when a part of data transmission network is damaged. Toughness is an important condition to measure the sturdiness of such FF-encoded network. In this work, we study the relationship between toughness and FCDG in IoV networks. Moreover, the graph conditions are considered together with the tight lower bound of the toughness for the existence of path factor. Such feasibility analysis of IoV networks help to find the bound in the effort to recover or realign lost links in networks, which is critical for the next generation of intelligent transportation systems where all vehicles are connected seamlessly.

Edge intelligence is an emerging technology that integrates edge computing and deep learning to bring AI to the network’s edge. It has gained wide attention for its lower network latency and better privacy preservation abilities. However, the inference of deep neural networks is computationally demanding and results in poor real-time performance, making it challenging for resource-constrained edge devices. In this paper, we propose a hierarchical deep learning model based on TreeNet to reduce the computational cost for edge devices. Based on the similarity of the classification categories, we decompose a given task into disjoint sub-tasks to reduce the complexity of the required model. Then a lightweight binary classifier is proposed for evaluating the sub-task inference result. If the inference result of a sub-task is unreliable, our system will forward the input samples to the cloud server for further processing. We also proposed a new strategy for finding and sharing common features across sub-tasks to improve training speed and accuracy. The experimental results on several popular datasets demonstrate the effectiveness of our approach in speeding up inferences while processing most of the input data with a low error rate.

Ethics in AI becomes a global topic of interest for both policymakers and academic researchers. In the last few years, various research organizations, lawyers, think tankers, and regulatory bodies get involved in developing AI ethics guidelines and principles. However, there is still debate about the implications of these principles. We conducted a systematic literature review (SLR) study to investigate the agreement on the significance of AI principles and identify the challenging factors that could negatively impact the adoption of AI ethics principles. The results reveal that the global convergence set consists of 22 ethical principles and 15 challenges. Transparency, privacy, accountability and fairness are identified as the most common AI ethics principles. Similarly, lack of ethical knowledge and vague principles are reported as the significant challenges for considering ethics in AI. The findings of this study are the preliminary inputs for proposing a maturity model that assesses the ethical capabilities of AI systems and provides best practices for further improvements.

Internet of Things based systems (IoT systems for short) are becoming increasingly popular across different industrial domains and their development is rapidly increasing to provide value-added services to end-users and citizens. Little research to date uncovers the core development process lifecycle needed for IoT systems, and thus software engineers find themselves unprepared and unfamiliar with this new genre of system development. To ameliorate this gap, we conducted a mixed quantitative and qualitative research study where we derived a conceptual process framework from the extant literature on IoT, through which 27 key tasks for incorporation into the development processes of IoT systems were identified. The framework was then validated by the means of a survey of 127 IoT practitioners from 35 countries across 6 continents with 15 different industry backgrounds. Our research provides an understanding of the most important development process tasks and informs both software engineering practitioners and researchers of the challenges and recommendations related to the development of next-generation of IoT systems.

Requirement selection is an essential component of software release planning, which finds, for a given budget, an optimal subset of the requirements with the highest value. However, due to the dependencies among software requirements, selecting or ignoring a requirement may impact the values of others. But such Value Dependencies are imprecise and hard to capture; they have been ignored by the existing requirement selection methods, increasing the risk of value loss in software projects. To address this, we have proposed a fuzzy-based optimization method with two main components: (i) a fuzzy-based technique for modeling value dependencies and capturing their imprecision, and (ii) an Integer Linear Programming (ILP) model that takes into account value dependencies in software requirement selection. The scalability and effectiveness of the method in mitigating value loss are demonstrated through simulations.

IoT (Internet of Things) platforms are key enablers for smart city initiatives, targeting the improvement of citizens’ quality of life and economic growth. As IoT platforms are dynamic, proactive, and heterogeneous socio-technical artefacts, systematic approaches are required for their development. Limited surveys have exclusively explored how IoT platforms are developed and maintained from the perspective of information system development process lifecycle. In this paper, we present a detailed analysis of 63 approaches. This is accomplished by proposing an evaluation framework as a cornerstone to highlight the characteristics, strengths, and weaknesses of these approaches. The survey results not only provide insights of empirical findings, recommendations, and mechanisms for the development of quality aware IoT platforms, but also identify important issues and gaps that need to be addressed.

The cloud computing literature provides various ways to utilise cloud services, each with a different viewpoint and focus and mostly using heterogeneous technical-centric terms. This hinders efficient and consistent knowledge flow across the community. Little, if any, research has aimed on developing an integrated process model which captures core domain concepts and ties them together to provide an overarching view of migrating legacy systems to cloud platforms that is customisable for a given context. We adopt design science research guidelines in which we use a metamodeling approach to develop a generic process model and then evaluate and refine the model through three case studies and domain expert reviews. This research benefits academics and practitioners alike by underpinning a substrate for constructing, standardising, maintaining, and sharing bespoke cloud migration models that can be applied to given cloud adoption scenarios.

Software systems development nowadays has moved towards dynamic composition of services that run on distributed infrastructures aligned with continuous changes in the system requirements. Consequently, software developers need to tailor project specific methodologies to fit their methodology requirements. Process patterns present a suitable solution by providing reusable method chunks of software development methodologies for constructing methodologies to fit specific requirements. In this paper, we propose a set of high-level service-oriented process patterns that can be used for constructing and enhancing situational service-oriented methodologies. We show how these patterns are used to construct a specific service-oriented methodology for the development of a sample system.