Test automation remains essential but increasingly brittle as applications evolve rapidly. This journal article introduces a semantic intelligence approach to test automation, enabling contextdriven adaptation through Natural Language Understanding (NLU), contextual reasoning, and machine learning. Unlike prior work that emphasizes locator-level self-healing alone, this study treats semantic understanding and business context awareness as first-class capabilities for resilient, self-adapting test systems. The framework learns from application behavior patterns, user workflows, and business rules to guide test execution, validation, and adaptation decisions. Through evaluation across 47 enterprise applications spanning finance, retail, healthcare, and media, we show that semantic reasoning can reduce test maintenance overhead by up to 85%, improve defect detection by 62%, and achieve 94% accuracy in autonomous UI change adaptation. A key contribution is semantic context graphs that map business intent to technical implementations, allowing tests to interpret not only “what” changed, but “why” it matters. We further introduce quantitative metrics for test-suite semantic drift and practical mitigation strategies. Finally, we discuss challenges in domain adaptation, computational efficiency, and interpretability, and propose solutions based on transfer learning and explainable AI techniques.
Autonomous software engineering agents are progressing from code assistants to systems that can plan and execute multi-step repository changes. This shift requires evaluation methods that move beyond one-shot pass rate reporting. Many current studies still provide limited visibility into repeatability, failure recovery, and operational efficiency under realistic constraints. This journal article presents an expanded DevAgentBench and DevAgentEval methodology designed for deployment-oriented assessment. The benchmark covers bug fixing, test generation, refactoring, code review assistance, and long-horizon feature work. We organize analysis into three metric layers: task-level success and correctness, robustness under perturbation, and business-aligned operational efficiency. We also formalize a nine-category failure-mode taxonomy linked to trace-level evidence and remediation guidance. Baseline experiments across agent patterns and model families show that rankings are sensitive to context reduction, tool-output noise, transient execution failures, and tighter resource budgets. These findings indicate that average success rates alone are insufficient for production decisions. We therefore recommend condition-aware reporting, repeated-run variance estimation, and reproducible artifact release as minimum standards for autonomous software-agent benchmarking.
Cloud computing is a major current trend that involves virtually distributing processing and data across configurable execution environments. Developing and deploying software for the cloud presents a new scientific challenge in terms of expressing and accounting for variability. Indeed, cloud computing relies on the principles of heterogeneity and elasticity, thus offering numerous configuration and scalability possibilities. Migrating existing applications therefore remains the primary challenge. Several types of migration exist, depending on the three services offered by the cloud. This article presents a study on application migration to the cloud in general, and more specifically on the migration of the "Information Access Assistance Service" (IA2S) application to cloud computing. Migrating existing systems to the cloud offers several advantages but also raises new challenges, particularly due to restrictions imposed by the service provider. Furthermore, factors such as time, training, and significant re-engineering make the migration process lengthy and prone to errors. Although several techniques exist for the partial or complete migration of existing applications to the cloud, only a few can overcome these constraints. Our work primarily consists, firstly, of clarifying the concepts of cloud and "Information Access Assistance Service" (IA2S), and then of studying existing migration strategies in order to propose the most suitable one for migrating IA2S to the cloud.
The integration of Machine Learning (ML) components into modern software systems enhances datadriven decision-making but introduces new challenges for Software Product Line (SPL) engineering. Variability modeling, configuration, and reuse become increasingly complex when adaptive ML components are involved. Although previous studies have addressed variability in traditional SPLs and ML integration in standalone systems, limited work has systematically explored the intersection of these two domains. This paper presents a structured framework that extends SPL engineering to support ML-aware variability management. The framework enables the systematic modeling and configuration of ML components and has been implemented in the VariaMos web tool. A case study demonstrates the framework’s feasibility and applicability, illustrating how it supports the development of adaptive and intelligent product lines.
This study investigates the integration of intelligent systems—such as AI-driven platforms and automated analytics tools—to modernize college curricula and align academic programs with the evolving needs of the labor market. Through real-time analysis of job trends, emerging skill demands, and industry innovations, the study identifies key gaps in current curricula. It also explores the feasibility of automating curriculum review and updates using intelligent tools. A prototype framework is proposed, with a focus on ethical principles including fairness, transparency, and data privacy. Preliminary results demonstrate that intelligent systems offer a scalable and effective solution for bridging the gap between higher education and workforce requirements. These systems leverage machine learning, data analytics, and decisionsupport mechanisms to continuously monitor labor market trends and adapt educational content accordingly. Their role is not limited to automation, but extends to dynamic interaction with curriculum developers and institutional policymakers to ensure curricular relevance and agility.
Although Agile methodologies emphasize decentralized decision-making and team autonomy, engineering managers continue to be employed in Agile software organizations. This apparent paradox suggests that traditional managerial functions persist despite the theoretical displacement of managerial hierarchy in Agile. This paper explores the persistence of engineering managers through a multidimensional framework encompassing historical context, theoretical tensions, organizational realities, empirical evidence, evolving managerial roles, and practical implications. A systematic literature review underpins our multifaceted analysis, supplemented by illustrative case studies. We conclude by proposing a conceptual model that reconciles Agile principles with managerial necessity, offering guidance for practitioners, researchers, and tool designers. Implications for leadership development, tool integration, and future research are discussed.
Controller synthesis is a theoretical approach to the systematic design of discrete event systems. It constructs a controller to provide feedback and control to the system, ensuring it meets specified control specifications. Traditional controller synthesis methods often use formal languages to describe control specifications and are mainly oriented towards single-agent and non-probabilistic systems. With the increasing complexity of systems, the control requirements that need to be satisfied also become more complex. Based on this, this paper proposes a controller synthesis method for semi-cooperative semi-competitive multi-agent probabilistic discrete event systems to solve the controller synthesis problem based on temporal logic specifications. The controller can ensure the satisfaction of specifications to a certain extent. The specification is given in the form of a linear temporal logic formula. This paper designs a controller synthesis algorithm that combines probabilistic model checking. Finally, the effectiveness of this method is verified through a case study.
The quick growth of cloud computing has caused a revolution in industries allowing companies to expand operations, boost productivity, and come up with new ideas at speeds never seen before. Yet, this growth has brought big environmental effects. Data centers, which form the core of cloud computing, are facilities that use a lot of energy and add a lot to global carbon emissions. By 2030, experts think the energy needs of data centers in just the United States will triple making up to 12% of the country's total power use (McKinsey, 2024). This big jump in energy demand shows we need to adopt sustainable cloud computing methods to reduce environmental harm while meeting industries' growing need for computing power. A big reason for this growing energy need is the more complex tasks we're doing those powered by AI and machine learning. These game-changing technologies need a lot of computing power, which puts more strain on data centers and energy systems. Just the use of generative AI is likely to need 50 to 60 more gigawatts (GW) of data center space in the U.S. by 2030 (McKinsey, 2024). This growth shows the two-fold challenge: to scale up cloud systems while also cutting down their carbon output.
The Aspect Oriented Software (AOS) paradigm emerged as a response to the limitations of ObjectOriented Programming, specifically its inability to modularize cross-cutting concerns effectively. However, AOS have inherent complexity that keeps increasing as software is modified and most of the existing metrics have not been theoretically or empirically validated. This means we cannot rely on them for measurement of AOS complexity. This paper proposes four base metrics and two composite coupling metrics for analyzing the complexity of AOS. The metrics were derived using the Entity-Attribute-MetricTool (EAMT) model. The metrics were theoretically validated using Briand’s framework, and a tool was developed to automate the computation of these metrics. Theoretical results indicate that the proposed metrics are mathematically sound. A between-subjects experimental study was conducted to validate the proposed metrics and results indicate that the proposed metrics are strongly correlated with modularity, meaning they are important for modularity assessment in AOS-based software.
Trends such as cloud computing raise issues regarding stable and uniform quality assurance and validation of software requirements. Current QA frameworks are poorly defined, often not automated, and lack the flexibility needed for on-demand, cloud based environments. These gaps lead to inconsistencies in service delivery, challenges in scaling organizational capacity, and internal and external inefficiencies that affect the reliability and effectiveness of cloud services. This paper presents a detailed framework for QA in cloud computing systems and advocates for standardized, automated, and adaptable systems to address these challenges. It aims to establish generic QA policies, incorporate intelligent techniques to enhance extendibility, and create adaptive solutions to manage the inherent attributes of cloud computing environments. The proposed framework is evaluated through survey questionnaires from industry practitioners, and descriptive statistics summarize the results. The study demonstrates the promise, effectiveness, and potential applicability of integrating a single QA framework to enhance the software functionality, dependability, and future adaptability in cloud computing systems
This paper presents a comprehensive framework for modeling and verifying multi-agent systems. The paper introduce an Epistemic Process Calculus for multi-agent systems, which formalizes the syntax and semantics to capture the essential features of agent behavior interactions and epistemic states. Building upon this calculus, we propose ATLE, an extension of Alternating-time Temporal Logic incorporating epistemic operators to express complex properties related to agent epistemic state. To verify ATLE specifications, this paper presents a model checking algorithm that systematically explores the state space of a multi-agent system and evaluates the satisfaction of the specified properties. Finally, a case study is given to demonstrate the method.
Pair programming, a fundamental practice in Extreme Programming and agile methodologies, is widely recognized for enhancing collaboration, improving code quality, and promoting knowledge sharing. This article explores the principles, benefits, and challenges of pair programming across traditional, hybrid, and large-scale agile environments. Drawing from empirical studies, case analyses, and real-world implementations, it highlights how pair programming fosters teamwork, accelerates problem-solving, and ensures adherence to coding standards. The effectiveness of this practice is influenced by factors such as task complexity, developer expertise, and alignment of team goals. In hybrid work settings, modern tools facilitate real-time collaboration, bridging gaps between in-person and remote participants. Despite challenges such as increased effort costs, role ambiguity, and technical barriers, pair programming remains a flexible and valuable methodology for achieving high-quality, maintainable software. The article underscores the importance of adapting pair programming practices to specific team dynamics and evolving work environments to maximize its impact on software development.
The complexity of IoT, owing to the inherent distributed and dynamic nature of such systems, brings more challenges to the software development process. A vast number of devices with different communication protocols and data formats is involved and needs to be connected and exchange data with each other in a seamless manner. Traditional software architectures fall short of addressing the requirements of IoT systems and, therefore, a new approach to software architecture is required. This paper presents an attempt to lay out the foundation for a quality attribute driven software architecture for the development of IoT systems. This architecture accommodates the appropriate architectural styles and design patterns necessary for the development of a robust IoT system. These include edge computing, microservices and event driven architectures. The proposed architecture treats IoT systems as autonomic systems which require a closed control loop to regulate and orchestrate the operational aspect of the IoT system.
The rapid proliferation of mobile applications across diverse platforms has introduced unprecedented challenges in ensuring optimal performance under varying conditions. Traditional performance testing techniques often struggle to address the complexity of mobile environments, characterized by diverse devices, dynamic network conditions, and resource constraints. This paper presents an AI-Driven Performance Testing Framework for Mobile Applications, designed to revolutionize the way performance bottlenecks are identified and addressed. The proposed framework leverages artificial intelligence to automate the testing process, dynamically adapt to real-world scenarios, and provide actionable insights for developers. Key innovations include AIpowered workload generation that mimics realistic user behaviors, anomaly detection to uncover hidden performance issues, and predictive analytics to anticipate future bottlenecks. The framework integrates seamlessly with CI/CD pipelines, ensuring continuous and scalable performance assurance. To validate its effectiveness, we conducted extensive evaluations across multiple mobile applications, demonstrating significant improvements in test accuracy, efficiency, and resource utilization. By addressing critical challenges such as device diversity, latency variability, and resource optimization, this research establishes a foundation for the next generation of performance testing tools tailored to the unique demands of mobile applications.
It is common for social scientists to consider building awareness interventions when faced with a social issue. The aim of which is to share knowledge about the issue at hand and inform society about the dangers, and possible responses. For example, raising awareness of healthy eating, including the benefits thereof and the dangers of lifestyle diseases. Or for raising awareness of information security, including the safe use of information technology resources about the dangers of technology abuse. One of the information security issues at hand is the bullying that takes place on mobile devices (mobile bullying), which has been increasing in recent years, especially among adolescents. The problem with mobile bullying is that it has severe consequences such as suicide and mental health difficulties. Despite current interventions, mobile bullying remains on the rise. The current study followed the design science research process to develop a digital artefact in the form of a chatbot to raise awareness of the mobile bullying social issue. The findings of this study reveal that information systems artefacts such as chatbots make awareness possible because they are easy to use, accessible to mobile device users, and allow for one-onone confidential conversations with the users in the privacy of their mobile devices. The use of a chatbot is also scalable and makes it easy to reach a large audience through mass communication of awareness messages. The conversational element allows the users to direct the conversation, as opposed to it being push notifications.
This paper explores the seven core principles of the Predictive Software Engineering (PSE) framework. These principles are designed to empower custom software development companies to deliver transparent and reliable solutions, all while adhering to predetermined budgets. The paper delves into each of the seven principles: Meaningful Customer Care, Transparent End-to-End Control, Proven Productivity, Efficient Distributed Teams, Disciplined Agile Delivery Process, Measurable Quality Management and Technical Debt Reduction, and Sound Human Development.
Competition forces software producers to offer their goods and services at the lowest costs in practice (49, p. 3), (4, p. 2). In this case, software errors can lead to additional processing costs which has to be covered by the software producers themselves (20). To compensate this, software manufacturers often have the option to cross-subsidize low-priced offer prices with offers billed hourly, e.g. on the basis of service contracts. However, the relation of possibilities for cross-subsidization with the risk of software errors is not clearly predictable by the process model (52, p. 9). As this relation is defined by the process, the aim to reach cost-control and cost-transparency is strongly related to the aim of (process) improvement (51, p. 39). In order to facilitate software producers within the calculation of minimum prices, required to cover the costs, an economical prediction model will be presented in this paper. This model bases on a simulation experiment, consisting of multiple scenarios. The scenarios were derived by a variation of the risik probability and the possibility of cross-subsidization.
The purpose of this work is to link a natural user interface and an autonomous navigation system to a conventional wheelchair with two control modalities: manual and automatic. It is taken into account that not all people have the same type or degree of disability, for this reason we seek to implement a user interface that involves different forms of user-machine interaction, with which the user can choose the manual mode that allows direct commands to control the movement of the chair and the automatic mode, which only needs the point or place where you need to go and through predefined trajectories, the autonomous navigation system will execute the necessary movements for the chair to follow the desired route.
The Levantine Basin is an important area for oil and gas exploration. However, the region's high salinity and corrosive conditions significantly threaten the durability of pipelines and equipment, resulting in substantial maintenance and repair expenses. This study seeks to evaluate the environmental and industrial influences on the corrosion rates of different oil and gas streams within the Levantine Basin. This study collected and analyzed data from the Levantine Basin, including physical and chemical properties of the streams, environmental factors such as temperature, pressure, and humidity. The Larkton corrosion software was utilized to simulate and predict corrosion rates under various conditions. Findings of this experiment demonstrate the significance of temperature on corrosion rates, showing a marked increase as temperatures rise. At lower temperatures, H₂S corrosion is more prominent, whereas CO₂ corrosion becomes the dominant factor at medium to high temperatures, especially when coupled with high CO₂ concentrations.
In the field of computer science, data compression is essential in the process of data transfer because it reduces file size without losing information. Issues that depend on the characteristics of the text can greatly affect the effectiveness of any compression algorithm. Consequently, each traditional compression algorithm has its own strengths and limitations. Therefore, a highly efficient compression method that achieves higher compression ratio is needed. In this paper, based on the sequential implementation of Huffman, RLE and LZW algorithms, a hybrid data compression method has been proposed. The algorithms have been individually evaluated and compared in terms of compression ratio and compressed file size. We conclude that in terms of compression ratio and compressed file size, LZW is better than Huffman and RLE. The results show that the proposed hybrid algorithm achieves the highest average compression ratio is 2.42 and the lowest average compressed file size is 1138.5 or less compared to the individual implementations of the algorithms. Thus, it can be concluded that the proposed hybrid method enhances lossless text compression in terms of compression ratio and file size according to compression metrics.
