ABSTRACT Introduction Requirements engineering (RE) faces challenges due to the handling of increasingly complex software systems. These challenges can be addressed using generative artificial intelligence (GenAI). Given that GenAI‐based RE has not been systematically analyzed in detail, this review examines the related research, focusing on trends, methodologies, challenges, and future work directions. Methods A systematic methodology for paper selection, data extraction, and feature analysis is used to comprehensively review 238 articles published from 2019 to 2025 and available from major academic databases. Results Although generative pretrained transformer models dominate current applications (67.3% of studies), the research focus remains unevenly distributed across RE phases, with analysis (30.0%) and elicitation (22.1%) receiving the most attention and management (6.8%) remaining underexplored. Three core challenges—reproducibility (66.8%), hallucinations (63.4%), and interpretability (57.1%)—form a tightly interlinked triad affecting trust and consistency, and strong correlations ( co‐occurrence) indicate that these challenges must be addressed holistically. Industrial adoption remains nascent, with > 90% of studies corresponding to early‐stage development and only 1.3% reaching production‐level integration. Evaluation practices show maturity gaps, limited tool/dataset availability, and fragmented benchmarking approaches. Conclusions Despite the transformative potential of GenAI‐based RE, several barriers hinder its practical adoption. The strong correlations among core challenges demand specialized architectures targeting interdependencies rather than isolated solutions. The limited real‐world deployment reflects systemic bottlenecks in generalizability, data quality, and scalable evaluation methods. Successful adoption requires coordinated development across technical robustness, methodological maturity, and governance integration. A multiphase research roadmap emphasizing evaluation infrastructure strengthening, governance‐aware development, and industrial‐scale standardization is proposed.

With the rapid advancement of virtual reality (VR) technology, understanding developer discussions is essential for improving software quality and maintenance. This study is the first to systematically investigate how developer concerns across major VR platforms, namely SteamVR, Meta, and HTCVive, align with the ISO/IEC 25010 international software quality standard. We collected and analyzed 392,590 posts from 47,280 developers, using topic modeling and manual coding to map discussions to nine ISO/IEC 25010 quality characteristics. We further examined topic distributions, sentiment trends, and interaction patterns across platforms. Our findings show that developers are most challenged by interaction, compatibility, and functionality issues, emphasizing the need to enhance user experience, enable cross device integration, and maintain system stability. Discussion on performance has decreased, signaling a shift in priorities. Platform specific challenges also emerged, highlighting the need for tailored strategies for different VR ecosystems. Based on these insights, we suggest strategies that prioritize optimizing user interaction (e.g., intuitive controls, seamless navigation), strengthening cross platform compatibility (e.g., universal SDKs, shared asset pipelines), and implementing sustainable maintenance practices (e.g., clear codebases, regular updates) to foster a more robust VR software ecosystem.

Object detection systems are embedded in many safety critical systems, such as autonomous driving systems. Assessing and minimizing the risks they introduce in these situations is a key concern. A universal definition of risk for all systems does not exist. Additionally, for a given system, the risk may change because it depends on the requirements, which evolve during development or production. This often results in multiple metrics being used, such as overall accuracy combined with the misclassification rate for a specified class. Existing research defines risk metrics for specific cases. However, the metric's meaning depends on the context. We propose RiskWeaver, a domain-specific language (DSL) to address these issues, which can handle various metrics and contexts of object detection. Specifically, RiskWeaver is used to define risks in object detection systems and to select datasets, which can improve a model's performance by considering risk.

Multi-View Modeling Framework for ML Systems (M3S) provides a framework to synchronize the experimental nature of machine learning and the deterministic side of traditional software engineering. However, understanding the framework's generality and limitations still requires further investigation. This paper compares the existing validation case study to a new case study of the OCT image diagnosis support system. The comparison between the two case studies shows M3S capability to handle variations in the nature of ML system analysis. However, the framework's capability to handle different ML tasks other than multi-class classification still requires further investigation.

The superior functionality and versatility of generative AI have raised expectations for the improvement of human society and concerns about the ethical and social risks associated with the use of generative AI. Many previous studies have presented risk issues as concerns associated with the use of generative AI, but since most of these concerns are from the user's perspective, they are difficult to lead to specific countermeasures. In this study, the risk issues presented by the previous studies were broken down into more detailed elements, and risk factors and impacts were identified. In this way, we presented information that leads to countermeasure proposals for generative AI risks.

We propose a conceptual framework, named "AI Security Continuum," consisting of dimensions to deal with challenges of the breadth of the AI security risk sustainably and systematically under the emerging context of the computing continuum as well as continuous engineering. The dimensions identified are the continuum in the AI computing environment, the continuum in technical activities for AI, the continuum in layers in the overall architecture, including AI, the level of AI automation, and the level of AI security measures. We also prospect an engineering foundation that can efficiently and effectively raise each dimension.

Advancements in large language models (LLMs) are transforming software engineering through innovative prompt engineering strategies. By analyzing prompt-driven enhancements across key software engineering tasks, we present a sys-tematic literature review and a pioneering taxonomy elucidating the practical applications of prompt engineering in software engineering. Our taxonomy offers a foundational framework that clarifies the roles of prompt engineering and measures its impact, thereby guiding evolving AI -driven software engineering research and practices.

One goal of technical online communities is to help developers find the right answer in one place. A single question can be asked in different ways with different wordings, leading to the existence of duplicate posts on technical forums. The question of how to discover and link duplicate posts has garnered the attention of both developer communities and researchers. For example, Stack Overflow adopts a voting-based mechanism to mark and close duplicate posts. However, addressing these constantly emerging duplicate posts in a timely manner continues to pose challenges. Therefore, various approaches have been proposed to detect duplicate posts on technical forum posts automatically. The existing methods suffer from limitations either due to their reliance on handcrafted similarity metrics which can not sufficiently capture the semantics of posts, or their lack of supervision (i.e., leveraging existing duplicate annotations) to improve the performance. Additionally, the efficiency of these methods is hindered by their dependence on pair-wise feature generation, which can be impractical for large amount of data. In this work, we attempt to employ and refine the GPT-3 embeddings for the duplicate detection task. We assume that the GPT-3 embeddings can accurately represent the semantics of the posts. In addition, by training a Siamese-based network based on the GPT-3 embeddings, we obtain a latent embedding that accurately captures the duplicate relation in technical forum posts. Our experiment on a benchmark dataset confirms the effectiveness of our approach and demonstrates superior performance compared to baseline methods. When applied to the dataset we constructed with a recent Stack Overflow dump, our approach attains a Top-1, Top-5, and Top-30 accuracy of 23.1 %, 43.9 %, and 68.9 %, respectively. With a manual study, we confirm our approach's potential of finding unlabelled duplicates on technical forums. We released our dataset and code in our supplementary package to promote further studies11Supplementary material package: https://github.com/mooselab/suppmaterial-PostDupGPT3.

In this study, we consider projects for developing service systems using machine learning (ML) techniques. These projects involve collaboration between various stakeholders. Several types of models representing system architectures are introduced so that stakeholders can develop a common understanding of these projects. In addition, metamodels are constructed by combining ML service systems models to provide project practitioners with a holistic view of the projects. In certain cases, these metamodels need to be extended to incorporate other business models for the business–IT alignment used in enterprises. For such situations, an enterprise architecture-based metamodel and method for managing the metamodel are proposed in this study, which provide a holistic view of business–IT alignment for ML projects. We confirm the effectiveness of the proposed metamodel and management method through real examples.

Abstract Development of machine learning (ML) systems differs from traditional approaches. The probabilistic nature of ML leads to a more experimentative development approach, which often results in a disparity between the quality of ML models with other aspects such as business, safety, and the overall system architecture. Herein the Multi-view Modeling Framework for ML Systems (M3S) is proposed as a solution to this problem. M3S provides an analysis framework that integrates different views. It is supported by an integrated metamodel to ensure the connection and consistency between different models. To facilitate the experimentative nature of ML training, M3S provides an integrated platform between the modeling environment and the ML training pipeline. M3S is validated through a case study and a controlled experiment. M3S shows promise, but future research needs to confirm its generality.

As machine learning expands to various domains, the demand for reusable solutions to similar problems increases. Machine learning design patterns are reusable solutions to design problems of machine learning applications. They can significantly enhance programmers' productivity in programming that requires machine learning algorithms. Given the critical role of machine learning design patterns, the automated detection of them becomes equally vital. However, identifying design patterns can be time-consuming and error-prone. We propose an approach to detect their occurrences in Python files. Our approach uses an Abstract Syntax Tree (AST) of Python files to build a corpus of data and train a refined Text-CNN model to automatically identify machine learning design patterns. We empirically validate our approach by conducting an exploratory study to detect four common machine learning design patterns: Embedding, Multilabel, Feature Cross, and Hashed Feature. We manually label 450 Python code files containing these design patterns from repositories of projects in GitHub. Our approach achieves accuracy values ranging from 80 % to 92% for each of the four patterns.

Machine learning system analysis requires different approaches for each different task and domain. Selecting a proper set of analytic models can be challenging for a specific problem. This paper discusses the extensibility of the Multi-View Modeling Framework for ML Systems approach using process mapping and extensible metamodel. We conducted a case study to evaluate the feasibility of such extensibility by extending the approach to facilitate an activity-driven analysis for an optical character recognition system. Based on the result of the case study, we found that Multi-View Modeling Framework for ML Systems is likely to be extensible.

Machine learning-based system requires specific attention towards their safety characteristics while considering the higher-level requirements. This study describes our approach for analyzing machine learning safety requirements top-down from higher-level business requirements, functional requirements, and risks to be mitigated. Our approach utilizes six different modeling techniques: AI Project Canvas, Machine Learning Canvas, KAOS Goal Modeling, UML Components Diagram, STAMP/STPA, and Safety Case Analysis. As a case study, we also demonstrated our approach for lane and other vehicle detection functions of self-driving cars.

We propose a new security risk assessment approach for Machine Learning-based AI systems (ML systems). The assessment of security risks of ML systems requires expertise in ML security. So, ML system developers, who may not know much about ML security, cannot assess the security risks of their systems. By using our approach, a ML system developers can easily assess the security risks of the ML system. In performing the assessment, the ML system developer only has to answer the yes/no questions about the specification of the ML system. In our trial, we confirmed that our approach works correctly.

Detecting software design pattern is an important part of software reverse engineering because design patterns can provide the most intuitive design idea of software products, which can be useful for maintenance engineers. In past studies, a lot of approaches have been proposed to detect design patterns, and the machine learning-based approach is a new trend in recent years. In this paper, we propose a preliminary idea of a deep learning-based approach to detect design patterns from UML class diagrams of software products, which can be used in some cases that traditional approaches may not work. We propose an overall process, which is divided into preparation phase and application phase. In preparation phase, we train a deep learning-based classifier to do the image classification task. In application phase, users may input the UML class diagram of a micro-architecture into the model and get the pattern it belongs to. We conduct a preliminary experiment to show the effectiveness of our approach, we train a Convolutional Neural Network (CNN) as the classifier and test it on our image dataset, which is constructed with UML images we collected from the Internet. We also use Gradient-weighted Class Activation Mapping (Grad-CAM) to do the visualization and use it to explain why our approach works. Lastly, we analyze the potential advantages and disadvantages of our approach.

Abstract During the initial stages of software development, the primary goal is to define precise and detailed requirements without concern for software realizations. Security constraints should be introduced then and must be based on the semantic aspects of applications, not on their software architectures, as it is the case in most secure development methodologies. In these stages, we need to identify threats as attacker goals and indicate what conceptual security defenses are needed to thwart these goals, without consideration of implementation details. We can consider the effects of threats on the application assets and try to find ways to stop them. These threats should be controlled with abstract security mechanisms that can be realized by abstract security patterns (ASPs), that include only the core functions of these mechanisms, which must be present in every implementation of them. An abstract security pattern describes a conceptual security mechanism that includes functions able to stop or mitigate a threat or comply with a regulation or institutional policy. We describe here the properties of ASPs and present a detailed example. We relate ASPs to each other and to Security Solution Frames, which describe families of related patterns. We show how to include ASPs to secure an application, as well as how to derive concrete patterns from them. Finally, we discuss their practical value, including their use in “security by design” and IoT systems design.

Cyber-physical systems (CPSs) are part of many critical infrastructures such as industrial automation and transportation systems. Thus, security incidents targeting CPSs can have disruptive consequences to assets and people. As incidents tend to re-occur, sharing knowledge about these incidents can help organizations be more prepared to prevent, mitigate or investigate future incidents. This paper proposes a novel approach to enable representation and sharing of knowledge about CPS incidents across different organizations. To support sharing, we represent incident knowledge (incident patterns) capturing incident characteristics that can manifest again, such as incident activities or vulnerabilities exploited by offenders. Incident patterns are a more abstract representation of specific incident instances and, thus, are general enough to be applicable to various systems - different from the one in which the incident originally occurred. They can also avoid disclosing potentially sensitive information about an organization's assets and resources. We provide an automated technique toextractan incident pattern from a specific incident instance. To understand how an incident pattern can manifest again in other cyber-physical systems, we also provide an automated technique toinstantiateincident patterns to specific systems. We demonstrate the feasibility of our approach in the application domain of smart buildings. We evaluate correctness, scalability, and performance using two substantive scenarios inspired by real-world systems and incidents.

The use of anomaly detection for log monitoring requires parsing model input features from raw, unstructured data. Log parsing methods come in many forms, but are generally categorized as being either offline or online. In this study, a systematic literature review of anomaly detection approaches utilizing online parsing methods is performed. An inventory of these approaches is taken, research gaps are explored, and suggestions for future exploration and study are presented.

To guide practitioners and researchers to design and research Machine Learning (ML) system development processes, we conduct a preliminary literature review on ML system development practices. We identified seven papers and two other papers determined in an ad-hoc review. Our findings include emphasized phases in ML system developments, frequently described ML-specific practices, and tailored traditional practices.

An assessment method is proposed to classify the reliability of business processes related to transactions into those with a high and low risk of inconsistency according to the checking status of transaction documents (such as an order form, a delivery slip, an invoice, a receipt). We show a method for objectively examining the reliability of business processes without relying solely on expert knowledge and experience. In practice, however, some departments in a company can be expected to execute transaction documents checking reliably, while others may not be able to. To cope with this, the method is extended so that assessment can be performed by reflecting the certainty of transaction documents checking for each department. Transaction documents checking for departments outside the company and outsourced departments can be excluded (such as a low-level department), and the risk of inconsistency in transaction documents can then be determined, thus improving the practicality of the assessment method.

The Smart Systems and Services Innovative Professional Education (Smart SE) program is a certification program developed as part of the education network for the Practical information Technologies (enPiT-Pro) project, which is funded by the Japan Ministry of Education, Culture, Sports, Science and Technology. The Smart SE program provides industry professionals working in fields related to information and communication technology (ICT) with additional training and education in smart systems and services that utilize various technologies such as IoT, Cloud, Big Data, and Artificial Intelligence (AI) for businesses. Here, we illustrate its purpose, curriculum and features to respond to the needs of industrial professional education.

To examine requirements and design of a system, using graphical models such as UML is one of the effective ways because it helps developers to understand the system and activities using the system. Usually, more than two types of notations are used to represent a system. At the age of digital transformation, relationships among several different systems should be also discussed and they are of course represented in several different notations. To improve the development and the analysis of several systems using such several notations, traceability among elements in the different notations should be managed, but most techniques focus on the traceability among a single project. In this paper, we present a tool to manage traceability on several different models. The tool is developed as a plugin of an existing graphical modeling tool called Astah. Astah enables us to describe UML models as well as mind maps, data flow diagrams, flow charts and so on. To evaluate our tool, we performed a method to elicit requirements of several different systems together by using the tool. We confirmed our tool was helpful to perform the method, but some additional functions would improve the performance more than now. The additional functions are as follows: tracing links transitively, annotating each link to clarify its type and recording an end of a link while the end is removed from a model.

Due to the widespread proliferation of today's Internet of Things (IoT), a system designer needs the IoT system and software design patterns to assist in designing scalable and replicable solutions. Patterns are encapsulations of reusable common problems and solutions under specific contexts. Many IoT patterns have been published, such as IoT design patterns and IoT architecture patterns to document the successes (and failures) in IoT systems and software development. However, because these patterns are not well classified, their adoption does not live up to their potential. To understand the reasons, we conducted a systematic literature review. From the 32 identified papers, 143 IoT architecture and design patterns were extracted. We analyzed these patterns according to several characteristics and outlined directions for improvements when publishing and adopting IoT patterns. Of the extracted patterns, 57% are non-IoT patterns, suggesting that IoT systems and software are often designed via conventional architecture and design patterns that are not specific to IoT design. Although most IoT design patterns are applicable to any domain, IoT architecture patterns tend to be domain specific, implying that the unique nature of IoT adoption in specific domains appears at the architecture level. As more domains adopt IoT, the number of domain-specific IoT design patterns should increase. In terms of quality attributes, many IoT patterns address compatibility, security, and maintainability.

Huge amount of techniques for creating, maintaining and/or recovering traceability among software development artifacts have been proposed. It is thus not easy for a potential user of such techniques to choose a technique suitable for his/her project because too many techniques exist and projects are different from each other. In this paper, we proposed a model for characterizing a traceability technique with respect to its users. The model can become a meta-model of the knowledge base for such users. The model is designed on the basis of the contents of existing technical papers so that we can easily describe model instances on the basis of technical papers. The model is represented in a feature model, and it has four mandatory features: source, destination, consequence and process. The user can at least understand a technique is unsuitable for him/her by referring such features. If a technique estimates the traceability relationships, the instance of its feature model may contain the quality metrics of its estimation such as precision and recall. It has also several optional features: assumptions, preprocess and tool. Although we sometimes cannot obtain such optional features from technical papers, they are so helpful for a user to decide a technique suitable for him/her. On the basis of the feature model, we described model instances of several techniques in technical papers. We also examined and discussed who the suitable user for each technique is.

A variety of methodologies to build secure systems have been proposed. However, most of them do not say much about how to evaluate the degree of security of their products. In fact, we have no generally-accepted ways to measure if the product of some methodology has reached some degree of security. However, if the system has been built with a methodology that uses patterns as artifacts, we believe that a simple evaluation is possible. We propose a metric for the security of systems that have been built using security patterns: We perform threat enumeration, we check if the patterns in the product have stopped the threats, and calculate the coverage of these threats by the patterns. We indicate how to take advantage of the Twin Peaks approach to arrive to a refined measure of security. In early work, we have proposed a secure systems development methodology that uses security patterns and we use it as example.

With the growing number of services on the Internet, the need for secure software development has increased. It is required for secure software development to consider security in the whole development life cycle. It is indispensable for secure software development to use various types of security knowledge. This study deals with security requirement analysis. Existing security requirements modeling systems do not provide a function to create an artifact while referring to security knowledge in an integrated manner. In this paper, the authors develop a modeling support system for a misuse case diagram that enables the association of knowledge with elements that constitute the diagram. The results of an experiment using the system show the system's usefulness in both the integration of the knowledge base with the artifact creation environment and the association of the knowledge with the elements of the diagram.

Traceability is important knowledge for improving the artifacts of software and systems and processes related to them. Even in a single system, various kinds of artifacts exist. Various kinds of processes also exist, and each of them relates to different kinds of artifacts. Traceability over them has thus large diversity. In addition, developers in each process have different types of purposes to improve their artifacts and process. Research results in traceability have to be categorized and analyzed so that such a developer can choose one of them to achieve his/her purposes. In this paper, we report on the results of Systematic Literature Review (SLR) related to software and systems traceability. Our SLR is preliminary one because we only analyzed articles in ACM digital library and IEEE computer society digital library. We found several interesting trends in traceability research. For example, researches related to creating or maintaining traceability are larger than those related to using it or thinking its strategy. Various kinds of traceability purposes are addressed or assumed in many researches, but some researches do not specify purposes. Purposes related to changes and updates are dominant.

Science computing platforms are changing from traditional on-premises computing platforms to clouds. The style of research publications is also changing with the movement of open science. For example, it is common that researchers in the bioscience research community publish their research papers with the associated research data on the Internet. Furthermore, demands for reproducibility of computational experiments are increasing. A conventional method, or sharing source codes of application programs, is not enough to guarantee the reproducibility of computational experiments. In this paper, we propose overlay cloud architecture for building virtual clouds on cloud platforms. In the proposed architecture, the middleware, Virtual Cloud Provider (VCP), automatically configures computing environments (application programs, operating systems, libraries and binaries) that is required to reproduce computing results by using the Linux container technology. VCP also deploys containers of computing environments on clouds and configures overlay network connecting the deployed containers. A case study which considers DNA sequencing software shows how the VCP can be deployed, and used to reproduce results.

We propose a metamodel for handling security and privacy in cloud service development and operation. The metamodel is expected to be utilized for building a knowledge base to accumulate, classify and reuse existing cloud security and privacy patterns and practices in a consistent and uniform way. Moreover the metamodel and knowledge base are expected to be utilized for designing and maintaining architectures for cloud service systems incorporating security and privacy.

An information system can store personal information of its primary users such as shopping histories, and some third party wants or happens to know such information. Because the system usually provides its privacy policy and its users have to give their consent to it, they sometimes have to partially give up the protection of their privacy. On the other hand, a chance of a third party to know such information is too limited if the policy is too defensive. We proposed a method to explore trade-offs between protection of such information and access permissions for a third party, and exemplified it. In this method, operation logs of a system are focused. The structure of each log is then modelled for analysing what kinds of information can be accessed by a third party. Access limitations of each third party are explored so as to balance the protection of privacy information against access right of third parties.

Because software developers are not necessarily security experts, identifying potential threats and vulnerabilities in the early stage of the development process (e.g., the requirement- or design-phase) is insufficient. Even if these issues are addressed at an early stage, it does not guarantee that the final software product actually satisfies security requirements. To realize secure designs, we propose extended security patterns, which include requirement-and design-level patterns as well as a new model testing process. Our approach is implemented in a tool called TESEM (Test Driven Secure Modeling Tool), which supports pattern applications by creating a script to execute model testing automatically. During an early development stage, the developer specifies threats and vulnerabilities in the target system, and then TESEM verifies whether the security patterns are properly applied and assesses whether these vulnerabilities are resolved.

In recent years, importance on software security technologies has been recognized and various types of technologies have been developed. On the other hand, in spite of recognition of necessity of providing cases that deal with full life cycle for secure software development, only few are reported. This paper describes a case-based management system (CBMS) that consists of an artifact management system and a knowledge-based management system (KBMS) to manage cases for secure software development. The former manages the artifacts created in secure software life cycle. The latter manages software security knowledge. The case-based management system also manages association between artifacts and software security knowledge and supports both visualization among software security knowledge and between artifacts and software security knowledge. We conducted an experiment to evaluate the system. We describe the effectiveness and future work of the system.

Although security patterns contain security expert knowledge to support software developers, these patterns may be inappropriately applied because most developers are not security specialists, leading to threats and vulnerabilities. Here we propose a validation method for security design patterns in the implementation phase of software development. Our method creates a test template from a security design pattern, which consists of the "aspect test template" to observe the internal processing and the "test case template". Providing design information creates a test from the test template. Because a test template is recyclable, it can create easily a test, which can validate the security design patterns. As a case study, we applied our method to a web system. The result shows that our method can test repetition in the early stage of implementation, verify pattern applications, and assess whether vulnerabilities are resolved.

Two basic approaches have been proposed to deal with cloud burst, disaster recovery, business continuity, etc., in community clouds: standardization of cloud services and multi-cloud federation. The standardization approach would take time; i.e., it would not be effective until there are enough implementations and deployments following the standard specifications. The federation approach places limitations on the functionalities provided to users; they have to be the greatest common divisor of the cloudsâ?? functions. Our approach is â??cloud on demandâ??, which means on-demand cloud extension deployments at remote sites for inter-cloud collaborations. Because we can separate the governance of physical resources for cloud deployment and the governance of each cloud by this approach, each organization can have full control on its cloud. We describe how the problems of the previous approaches are solved by the new approach and evaluate a prototype implementation of our approach.

Software developers are not necessarily security specialists, security patterns provide developers with the knowledge of security specialists. Although security patterns are reusable and include security knowledge, it is possible to inappropriately apply a security pattern or that a properly applied pattern does not mitigate threats and vulnerabilities. Herein we propose a method to validate security pattern applications. Our method provides extended security patterns, which include requirement- and design-level patterns as well as a new model testing process using these patterns. Developers specify the threats and vulnerabilities in the target system during an early stage of development, and then our method validates whether the security patterns are properly applied and assesses whether these vulnerabilities are resolved.

In a Web application framework suitable for a code-centric development approach, maintaining the faultlessness of the security features is an issue because the security features are dispersed throughout the code during the implementation. In this paper, we propose a method and develop a static verification tool for Web applications that checks the completeness of the security features implementation. The tool generates a navigation model from an application code while retaining the security properties and then checks the consistency of the security properties on the model since access control is relevant to the application behavior. We applied the proposed tool to various Ruby on Rails Web application source codes and then tested their authentication and authorization features. Results showed that the tool is an effective aid in the implementation of security features in code-centric and iterative Web application development.

We propose a model-assisted security testing framework for developing Web applications. We devised a tool called “RailroadMap” that automatically extracts a behavior model from the code base of Ruby-on-Rails. This model provides a unified point of view for analyzing security problems by representing an application's behavior, which includes all security functions and possible attack scenarios.

Cloud computing environments have certain techniques to make applications auto-scale according to the load. The environment is also used for disaster recovery. For instance, environments may allow applications to move to other cloud environments in the event of disasters. Some applications, like continuous integration tests, need to have execution environments that depend on their test running context. Currently, these application types are treated individually for development. However, they can be developed as self-deployable applications and the Cluster as a Service foundation supports them. This paper shows the possibility of this idea and discusses a prototype foundation.

Cluster computing has become popular in academia and industry. Clusters of commodity servers are used for a variety of distributed applications like simulation, data analysis, and web services. No single framework can fit every distributed application. Even an Infrastructure as a Service (IaaS) like the Openstack cloud framework cannot fit all of them because, for example, some researchers would likely want to deploy hadoop clusters on bare metal servers. One solution to this problem is to assign a layer below the IaaS/PaaS (Platform as a Service) layer the job of handling cluster deployment. This idea is called the Cluster-as-a-Service layer for allocating servers and deploying IaaS and PaaS. This paper introduces dodai-deploy, which handles Cluster as a Service deployments.

A good way to obtain secure systems is to build applications in a systematic way where security is an integral part of the lifecycle. The same applies to reliability. If we want a system which is secure and reliable, both security and reliability must be built together. If we build not only applications but also middleware and operating systems in the same way, we can build systems that not only are inherently secure but also can withstand attacks from malicious applications and resist errors. In addition, all security and reliability constraints should be defined in the application level, where their semantics is understood and propagated to the lower levels. The lower levels provide the assurance that the constraints are being followed. In this approach all security constraints are defined at the conceptual or application level. The lower levels just enforce that there are no ways to bypass these constraints. By mapping to a highly secure platform, e.g., one using capabilities, we can produce a very secure system. Our approach is based on security patterns that are mapped through the architectural levels of the system. We make a case for this approach and we present here three aspects to further develop it. These aspects include a metamodel for security requirements, a mapping of models across architectural levels, and considerations about the degree of security of the system.

Unlike functional implementations, it is difficult to analyze the impact software enhancements on security. One of the difficulties is identifying the range of effects by new security threats, and the other is developing proper countermeasures. This paper proposes an analysis process that uses two kinds of security pattern: security requirements patterns (SRP) for identifying threats and security design patterns (SDP) for identifying countermeasures at an action class level. With these two patterns and the conventional traceability methodology, developers can estimate and compare the amounts of modifications needed by multiple security countermeasures.

Hironori Washizaki, Waseda University/GRACE Center, National Institute of Informatics, 3-4-1, Okubo, Shinjuku-ku, Tokyo, Japan, [email protected] present two common patterns for distributed systems: Enterprise Service Bus (ESB) and Distributed Publish/Subscribe (P/S). ESB defines a common bus structure that provides basic brokerage functions as well as a set of other appropriate services. The ESB has been used mostly for web services but it can be used for any distributed system. The P/S realizes a system structure where subscribers register to receive events produced by a publisher. The P/S has been described usually in a centralized environment and we emphasize here its distributed nature. These patterns are mainly intended for web services application and distributed systems architects and designers. In those applications, the ESB and the Distributed P/S are architectural units that need to be combined with other architectural units.

A wireless sensor network (WSN) consists of spatially distributed nodes that monitor physical conditions. Thus far, WSNs have been constructed for specific applications. However, in the future, system operators may want an existing WSN to deploy simultaneous applications. Existing studies have demonstrated the effectiveness of applying mobile agents to enable simultaneous applications in WSNs. However, two problems make the present form of mobile agents impractical for applications involving cooperation and group migration by mobile agents. The first problem is the lack of a suitable architecture enabling cooperation and interaction between agents, and the second is the lack of an efficient way of achieving group migration by agents in WSNs. To address these problems, we propose an architecture organized by mobile agents that has specific functionalities and that enables group migration by multiple agents.

It is possible to reasonably measure the security quality of individual security patterns. However, more interesting is to ask: Can we show that a system built using security patterns is secure in some sense? We discuss here some issues about evaluating the security of a system built using security patterns. We consider the use of threats and misuse patterns to perform this evaluation.

The spread of open-software services through the Internet increases the importance of security. A security pattern is one of the techniques in which developers utilize security experts' knowledge. Security patterns contain typical solutions about security problems. However there is a possibility that developers may apply security patterns in inappropriate ways due to a lack of consideration on dependencies among patterns. Application techniques of security patterns that consider such dependencies have not been proposed yet. In this paper, we propose an automated application technique of security patterns in model driven software development by defining applications procedures of security patterns to models as model transformation rules with consideration for pattern dependencies. Our technique prevents inappropriate applications such as the application of security patterns to wrong model elements and that in wrong orders. Therefore our technique supports developers apply security patterns to their own models automatically in appropriate ways.

Peer-To-Peer-systems (P2P) introduced new methods to distribute large amounts of data to end users. To increase the distribution speed resources from all participating network nodes, the peers, are used, and therefore the workload on own resources decreases. To utilize all peers large data is split into small pieces, so called chunks, and these chunks are distributed among peers therefore making each chunk available on different peers. To identify and find chunks in P2P-systems hash algorithms are used, and each peer is responsible for a specific range of the hash's keyspace and all chunks that fall within this keyspace.

Security patterns are now starting to be accepted by industry. Security patterns are useful to guide the security design of systems by providing generic solutions that can stop a variety of attacks but it is not clear to an inexperienced designer what pattern should be applied to stop a specific attack. They are not useful either for forensics because they do not emphasize the modus operandi of the attack. To complement security patterns, we have proposed a new type of pattern, the misuse pattern. This pattern describes, from the point of view of the attacker, how a type of attack is performed (what units it uses and how), defines precisely the context of the attack, analyzes the ways of stopping the attack by enumerating possible security patterns that can be applied for this purpose, and describes how to trace the attack once it has happened by appropriate collection and observation of forensics data. We present here a model that characterizes the precise structure of this type of pattern.

Although numbers of software pattern catalogues and languages have been published, little is known about quality of patterns, quality by patterns and quality aspects of pattern activities. This workshop seeks to gain an improved understanding on the theoretical, social, technological and practical issues related to quality aspects of patterns including security and safety.

This paper discusses the Top SE program established to bridge the industry-academia gap. The program features extensive use of software engineering tools, not only to introduce students to the tools, but also as a conduit for learning the techniques and guidelines needed to apply the tools to practical software development situations. The curriculum is organized around practical problems mainly from the area of digital home appliances and focuses on upper stream software development processes. The Top SE program is developed and operated by a close collaboration between industry and academia. We illustrate our discussion with examples from one of the courses, verification of design models, which takes up model checking technologies, including three specific tools: SPIN, SMV, and LTSA.

BPEL and similar languages have been provided a foundation for process-based implementation of composite Web services. These languages allow definition of orchestration processes without concrete binding information of involved partners. Although this approach facilitates customization of service discovery and selection before process execution, partner management during process execution has not been considered, such as rebinding of service providers upon events. In response to this problem, this study proposes a description model that facilitates runtime partner management in process-based services. The proposed model includes policy descriptions that allow insertion and customization of binding behavior according to surrounding environments and user preferences. The model also includes extensions in the standard process notation in order to allow process developers to give constraints to avoid adoption of inadequate policies as well as to define handlers to have common rebinding behavior reused. The proposed descriptions have been implemented and used in an agent framework for multimedia services.

A location-specific data retrieval, which is data retrieval from nodes in a designated region at the time, is an attractive application in a mobile ad-hoc network (MANET). However, almost all nodes in a MANET are powered by batteries, the location-specific data retrieval should involve a small number of messages. In this paper, we use a mobile agent to retrieve the location-specific data. A mobile agent migrates to a node in a designated region, and retrieves data from nodes in this region. Since, after migration, the agent can communicate with nodes in the designated region through low overhead short length hops, the mobile agent can retrieve data at low message cost for long periods, even if the owner of this agent moves around. However, even after migrating to node in the designated region, in order to stay near this region, a mobile agent should migrate to other nodes in response to the movement of the node hosting this agent. In this paper, we propose the geographically bound mobile agent (GBMA) which is a mobile agent that periodically migrates in order to always be located in a designated region. In order to clarify where the GBMA should be located and when the GBMA starts to migrate, two geographic zones are set to the GBMA: required zone and expected zone. The required zone ease tracking of the GBMA, and the expected zone ease adjustment of the GBMA migration timing.

Web services technology for interoperability, dynamic discovery and integration of distributed components is to be applied in various environments, such as networks of relatively narrow and unstable wireless connections. However, in such environments it is necessary to deal with resource limitation. Our work adopts mobile agent technology in response to this problem and presents a mobile agent framework for Web Services integration. This framework utilizes BPEL (Business Process Execution Language for Web Services) for description of the integration logic, and provides declarative descriptions for physical behaviors (migration and cloning). This facilitates addition or change of physical behaviors according to the environmental conditions while preserving the integration logic. This paper especially focuses on formal definition and discussion of our framework.

As mobile computing becomes common, the battery issue of mobile computing devices has become increasingly notable. To this end, research and development of various power-conservation devices and methods are actively taking place. However, the conventional method of extending the battery life through power-conservation can never prevent the unintentional shutdowns of applications due to a dead battery. This research aims to realize the evacuation of applications on a mobile computing device to another device before the battery runs out by creating the application as a mobile agent. In particular, by introducing the concept of the Crisis Management Center, dynamic and smooth evacuation of multiple application agents will become possible. The paper explains and verifies the effectiveness of the EASTER (Escape Agent System from dying batTERy), a system developed for the purpose of recovering the applications when a battery is running out through the use of a mobile agent system.