The Security Implications of Microservices in Modern Software Development

Microservices architecture has emerged as a transformative paradigm in modern software development, enabling organizations to build resilient and scalable distributed systems. This comprehensive exploration delves into the fundamental principles, implementation strategies, and real-world applications of microservices architecture. The architectural framework facilitates independent service deployment, enhanced fault isolation, and streamlined maintenance processes while promoting technology stack flexibility. Through the implementation of sophisticated monitoring patterns, testing strategies, and data consistency mechanisms, organizations can effectively address common challenges in distributed systems. The integration of enterprise architecture principles with microservices has demonstrated significant improvements in resource utilization, system reliability, and operational efficiency. The adoption of API gateway patterns, security frameworks, and containerization technologies further enhances system performance and scalability. Real-world applications, particularly in e-commerce platforms, showcase the practical benefits of microservices architecture in managing complex business operations while maintaining high availability and performance standards.

Security Architecture Patterns for High-Volume Distributed Enterprise Platforms

Modern software development is highly knowledge intensive; it requires that software developers create and share new knowledge during their daily work. However, current software development environments are “syntantic”, i.e. they do not facilitate understanding the semantics of software artifacts and hence cannot fully support the knowledge-driven activities of developers. In this paper we present SoWiSE, a Semantic Wiki environment which focuses on the software development domain and strives to address these problems. SoWiSE aims at providing software developers such a tool to ease their daily work and facilitate cooperation, context-aware navigation, ontology-based browsing and searching, concept-based source code documentation and related problem solving.

This comprehensive review examines the link between AI and software development by examining their interplay throughout all development life cycle stages. The research combines insights from current literature and industry practices, to shed light on AI's impact on processes such as requirement analysis, design, coding, and testing. The study uses the Review Paper technique for examination, covering a wide range of sources, including academic databases, research portals, and publications. The seven-week timeframe includes data analysis, a study of the literature, and a critical examination of the implications and effective role of AI. The study indicates that AI is a collaborative tool in software development, based on historical and contemporary technical realities. The study not only gives more detailed knowledge but also establishes the framework for future research. underlining the need for industry-specific AI applications and a better evaluation of developers' experiences. This research acts as a guidepost for industry executives and academics, providing insights into the changing landscape influenced by AI on software development

Microservice architecture (MSA) is a type of software and systems architecture that is based on the modularization principle. It proposes designing systems employing small-scaled, loosely coupled, and independently deployable microservices. There are several benefits of microservices architecture in terms of maintainability, scalability, and productivity which have led to rise in its popularity. Even though there are several studies about development in MSA, the studies on the quality of the microservice-based systems are limited. In this study, we propose a quality model based on fuzzy logic to measure and assess quality attributes of systems in MSA that can be used by software architects, developers, and project managers. We focus on maintainability of microservices because it is one of the most important quality attributes of software systems. We identified sub-characteristics and properties of microservices that affect maintainability, and constructed a hierarchical quality model based on ISO/IEC 250xy standard SQuaRE (System and Software Quality Requirements and Evaluation). Our fuzzy model measures maintainability of microservices in three levels, i.e., low, medium, and high. We provided a basis for the development and application of quality models in industrial practice as well as a basis for further extension. To demonstrate and evaluate our methodology, we used open-source applications designed in MSA. The results show that our method can assess maintainability of microservices realistically.

This paper focuses on the systematic mapping of monolithic applicationsto microservices architecture, a popular alternative in the software development industry.The research examines the advantages of microservices and the challenges organizationsencounter during the transition from monolithic systems. A case study of a financialapplication is presented, along with proposed techniques for identifying microserviceswithin monolithic systems using domain-driven development concepts.The study highlights the difficulties and benefits of migrating from monolithic to microservicesarchitecture, offering valuable insights for software architects and developers. Practicalimplications include a technique for identifying microservices on monolithic systemsusing domain-driven concepts and various communication protocols for service interaction.The findings suggest that this proposed technique can enhance work performanceand establish clear models, particularly for complex systems. However, it may have limitedeffectiveness in less complex systems. The paper contributes to the field of softwaredevelopment by providing practical solutions for companies considering a shift tomicroservices architecture and comparing the two architectural styles.

Third-party libraries are used frequently in modern software development. Websites that are visited millions of times a day often make heavy use of open-source software libraries. As a software developer, using these libraries has a number of advantages. Developers save time and effort because functionality does not have to be rebuilt from scratch. Additionally, these libraries are often completely free to use, have been tested extensively by other users and contain a large amount of expertise of other developers. There are also a number of disadvantages to using these libraries. When the interface of a software library changes, this has direct impact on software systems using this interface. In this thesis, the origin, impact and costs of interface instability is investigated. As it turns out, public interfaces of software libraries frequently change without providing proper signals to users of that interface. These changes have measurable impact in systems using these interfaces. Besides changes in interfaces, changes in functionality can also impact end users of a library. In this thesis, compression is employed to measure changes in functionality that a library provides. These changes in functionality are then used in a software growth model that relates the concepts of software growth, quality, work done and size to each other.

AI and Computer Engineering are combining to revolutionize software development. They encourage creativity, enhance functionality, and increase efficiency. As AI-driven tools and frameworks become indispensable to the development of today, their merging with CE is changing the game and making it possible to create intelligent, adaptable systems that can solve the most challenging problems in multiple industries. This study aims to investigate where artificial intelligence and computer engineering intertwine in modern software development, specifically by looking into methods that enhance innovation, functionality, and efficiency in software through integrated AI-driven tools, frameworks, and performance measures.A three-phase mixed-methods approach was adopted. The secondary data analysis included pertinent literature and open-source code repositories, structured surveys, and interviews of fifty industry professionals. While qualitative analysis was done by applying thematic coding to find trends, obstacles, and possibilities in AI integration, quantitative analysis focused on performance indicators such as execution speed, accuracy, and memory utilization. The most widely used AI frameworks are TensorFlow and PyTorch due to their performance and flexibility. The industry-specific trends have been identified with different priorities: healthcare focuses on accuracy (77.5%), retail on the execution speed (55 ms) and minimal resource usage, and finance on balanced optimization. Major challenges were seen in the form of barriers such as a high learning curve (20%), scalability issues (25%), and compatibility issues (30%).The report emphasizes that better tools, increased cross-platform support, and thorough training are needed to overcome the hurdles related to the adoption of AI. It identifies industry-specific needs for AI software optimization and provides useful advice on how to successfully use AI technology in sectors like healthcare, retail, and finance. Specific Contribution: This study provides practical insights into how AI is revolutionizing software development. It provides a strong analytical framework to assess performance metrics and solve integration issues by analyzing industry-specific trends and trade-offs in AI integration. This advances our understanding of CE-AI convergence and guides future research and development in AI-augmented systems.

Putting the user at the center of the process has been considered key to achieve quality in modern software development. Discovering and framing the user needs are necessary activities to produce better quality software that fits the customer’s needs and their satisfaction. User-centered Design approaches such as Design Thinking (DT) help to establish a creative mindset through convergent and divergent working spaces that allow for the team to better understand, to create empathy, to brainstorm ideas, to prototype, and to test innovative solutions in software development. However, navigating in this world is still challenging. Literature reports on the use of a large spam of techniques to support DT. Selecting an appropriate set of techniques considering the project context, the stakeholders’ profiles, and other characteristics may be challenging, mainly to novice professionals. Thus, as part of an on-going long-term research project, we propose Helius, a collaborative recommender system for DT techniques in software development. Helius provides features supporting recommendations of DT Techniques based on the user previous experiences, techniques’ evaluation ratings, and community feedback. This paper focuses on our preliminary results upon the tool proposal–on a comparison of Helius features with other related tools using the DESMET method. This comparison shows that Helius brings new features for the recommendation of DT techniques, thus, contributing to process quality improvement.

Many scientific disciplines rely on computational methods for data analysis, model generation, and prediction. Implementing these methods is often accomplished by researchers with domain expertise but without formal training in software engineering or computer science. This arrangement has led to underappreciation of sustainability and maintainability of scientific software tools developed in academic environments. Some software tools have avoided this fate, including the scientific library Rosetta. We use this software and its community as a case study to show how modern software development can be accomplished successfully, irrespective of subject area. Rosetta is one of the largest software suites for macromolecular modeling, with 3.1 million lines of code and many state-of-the-art applications. Since the mid 1990s, the software has been developed collaboratively by the RosettaCommons, a community of academics from over 60 institutions worldwide with diverse backgrounds including chemistry, biology, physiology, physics, engineering, mathematics, and computer science. Developing this software suite has provided us with more than two decades of experience in how to effectively develop advanced scientific software in a global community with hundreds of contributors. Here we illustrate the functioning of this development community by addressing technical aspects (like version control, testing, and maintenance), community-building strategies, diversity efforts, software dissemination, and user support. We demonstrate how modern computational research can thrive in a distributed collaborative community. The practices described here are independent of subject area and can be readily adopted by other software development communities.

Facing Global Environmental Change

The conventional Software Configuration Management (SCM) tools have shown their weakness in large and complex software systems with the development of modern software. This paper provides a better SCM tool with high availability to facilitate software development process. A novel approach named Quality of Service (QoS)-based SCM (QSCM) is presented, which introduces QoS guarantee techniques at the application level to web-based SCM system in order to improve SCM system?s performance. QSCM classifies users together with SCM activities and treats with requests according to their priority. When the server is overloaded, it stops treatment with new requests so that to avoid from crash. The work described here is a first step towards this goal.

For many years now, modern software is known to be developed in multiple languages (hence termed as multilingual or multi-language software). Yet, to date, we still only have very limited knowledge about how multilingual software systems are constructed. For instance, it is not yet really clear how different languages are used, selected together, and why they have been so in multilingual software development. Given the fact that using multiple languages in a single software project has become a norm, understanding language use and selection (i.e., language profile ) as a basic element of the multilingual construction in contemporary software engineering is an essential first step. In this article, we set out to fill this gap with a large-scale characterization study on language use and selection in open-source multilingual software. We start with presenting an updated overview of language use in 7,113 GitHub projects spanning the 5 past years by characterizing overall statistics of language profiles, followed by a deeper look into the functionality relevance/justification of language selection in these projects through association rule mining. We proceed with an evolutionary characterization of 1,000 GitHub projects for each of the 10 past years to provide a longitudinal view of how language use and selection have changed over the years, as well as how the association between functionality and language selection has been evolving. Among many other findings, our study revealed a growing trend of using three to five languages in one multilingual software project and the noticeable stableness of top language selections. We found a non-trivial association between language selection and certain functionality domains, which was less stable than that with individual languages over time. In a historical context, we also have observed major shifts in these characteristics of multilingual systems both in contrast to earlier peer studies and along the evolutionary timeline. Our findings offer essential knowledge on the multilingual construction in modern software development. Based on our results, we also provide insights and actionable suggestions for both researchers and developers of multilingual systems.

In order to adapt the change from centralized development pattern to multi-user collaborative development pattern in modern software development, the concept of software collaborative development was proposed by introducing CSCW (computer-supported cooperative work) into the field of software development, and the properties of it was analyzed, then a formalized model SCP-NET of software collaborative development based on Petri net was founded. The establishment of model is based on the following two aspects: first, introducing the hierarchy mechanism, the complexity of the model was reduced; second, four states (ready, running, suspended and completed) of transition was introduced, the model could promote the coordination of the sub process in software collaborative development. Then the transition firing rule based on the model was proposed, it establishes the foundation for further analyzing, validating and improving the software collaborative development process model. Finally, the conclusion that the model SCP-NET can support the six important properties was validated.

Artificial Intelligence (AI) is rapidly redefining the software engineering landscape.It enhances development speed, accuracy, and scalability across various phases of the Software Development Life Cycle (SDLC).From automating mundane coding tasks to intelligent debugging and project optimization, AI empowers developers to focus on innovation.Recent advancements in generative AI, machine learning, and natural language processing have introduced tools that assist with code generation, automated software testing, bug detection, and agile management.This research synthesizes insights from eleven notable IEEEindexed papers published between 2013 and 2024.Each of these papers explores different dimensions of AI's influence in software development-from secure DevOps and test automation to reuse of software components and explainable AI systems.Our findings confirm that the integration of AI into software processes boosts productivity, reduces error rates, and encourages modular development.However, certain limitations like lack of transparency, ethical concerns, and reliance on historical training data persist.This paper aims to present a well-rounded perspective on the current role and future potential of AI in software engineering.Furthermore, the adoption of AI in software engineering is not just limited to automation but is also influencing architectural decisions and continuous integration practices.By integrating AI into DevOps pipelines, development teams gain real-time insights, predictive analytics, and adaptive responses to changing requirements.The role of AI in enhancing developer productivity, improving software quality, and enabling faster time-to-market is becoming increasingly critical in today's competitive and agile-driven software industry