UML state machine diagrams describe event-driven behavior, but novices struggle to understand how syntactic elements relate to semantics, making learning difficult. Blockbased programming environments such as Scratch, by contrast, are familiar to younger learners and provide an accessible, executable way to explore computational concepts, allowing trial-and-error execution that may ease this understanding. Motivated by this potential, we present a block-based educational tool to support novice learning of state machine representation. Built on Blockly, the tool provides state-machine-oriented blocks that novices can assemble into executable behaviors. To aid comprehension, assembled blocks can be executed with logs and transformed into UML state machine diagrams in PlantUML, linking block assemblies with state machine modeling. In this demonstration, we highlight features that reduce burden for instructors and novices: easy deployment, automatic import of modeling elements (e.g., state names and triggers) defined in JSON as blocks, immediate execution with logs, and diagram generation. As a preliminary evaluation, we surveyed 8 university students with prior exposure to state machine diagrams. While some difficulties arose with specific blocks, no major issues were found. These results suggest that block-based representation may help novices learn state machine concepts more smoothly and support gradual introduction to UML notation. The prototype tool has been released for public access at https://github.com/ 25w6051b/BlockSM, and a demonstration video is also available at https://youtu.be/ZzVBhjWq1zY.

Stakeholders want quality in use requirements to be satisfied as much as possible to make their life or business activities better than ever. However, some of them should be abandoned if they are expensive. In this paper, we propose a method for analyzing the cost ratio of quality in use for a life or business system during early requirements analysis. This method enables stakeholders to sort out initially required quality in use requirements. The cost depends on the number and the complexity of operations in a system, their data and users. In this method, KAOS model is thus used because it contains goals, operations, users and data. KAOS model also enables us to represent the roles of both human and artificial systems embedded in a business system. When a part of a system should achieve some functional and quality goal, the goal should be transformed into some operations and their data respectively. The relative cost of some function or quality is thus quantified on the basis of operations, data and associations among them. Through the case study, the results of analysis seemed to meet our intuition. In addition, we found the relative cost of quality in use is not so small. This finding makes stakeholders to abandon some of quality in use in this case if they do not have enough budget and/or time.

The extraction of non-functional requirements (NFRs) is a crucial yet complex task in information system development, often requiring extensive domain expertise. Traditional persona-driven approaches rely on expert-driven manual processes, which can be inconsistent and inefficient. To address this challenge, we propose an LLM-based autonomous agent framework for persona-driven NFR extraction, integrating Retrieval-Augmented Generation (RAG) to enhance contextual adaptation. The framework systematically automates persona generation, scenario-based experience simulation, interview question formulation, response evaluation, and final NFR specification, incorporating iterative refinement mechanisms. To evaluate its effectiveness, we conducted a case study using a real-world procurement specification, comparing RAG-enabled and non-RAG conditions. Experimental results demonstrate that RAG improves the contextual relevance of interview questions, with an average Context Relevancy Score of 0.5135 compared to 0.4795 without RAG. Additionally, RAG-enabled interviews exhibited broader information coverage in the initial round, as indicated by Convex Hull Volume analysis. However, dynamic flow control mechanisms ensured that non-RAG conditions, through iterative refinement, achieved comparable final NFR completeness. Human evaluation confirmed that the proposed framework generates consistent, structured, and comprehensive NFR specifications while reducing dependency on manual expertise. These findings highlight the potential of integrating LLM agents and RAG to enhance automation and coverage in NFR extraction. Future work will focus on refining adaptive RAG application mechanisms and optimizing persona-driven question generation strategies to further improve the efficiency and accuracy of NFR specification processes.

The development of service systems leveraging Machine Learning (ML) has been actively pursued in recent years. In the development of general service systems, knowledge for effectively managing projects has been systematized. Similarly, in ML service system development projects (ML projects), knowledge such as best practices and patterns is being established. Among these, anti-patterns documenting situations that cause issues in ML projects and their corresponding solutions, are also being organized. However, knowledge like patterns and anti-patterns does not clearly define who should recognize their necessity or apply the solutions. As a result, in ML projects, where collaboration among stakeholders is critical, the utilization of such knowledge often depends on the experience and skills of the individuals involved. In this study, we propose a diffusion model for anti-patterns in ML projects and a method for representing the model. Furthermore, by representing actual anti-patterns as a diffusion model using the proposed method, we verify the effectiveness of the represented model.

Common Vulnerability Scoring System (CVSS) is a standard method for quantifying the severity of software vulnerabilities. Security engineers can identify the underlying causes of vulnerabilities and develop countermeasures based on CVSS base vector and their related scores. The CVSS base vector is a set of metrics used to calculate the severity score of a vulnerability based on its intrinsic characteristics, such as “Attack Vector (AV)” and “Privilege Required (PR)‘. Security experts often take a few weeks to manually determine the CVSS base vector of a new vulnerability report. Consequently, the CVSS base vector is usually unknown for a period after the report is released, and thus delaying vulnerability countermeasures. Therefore, several methods for predicting CVSS base vector have been proposed. This paper compares the performance among methods that use BERT, multinomial logistic regression, or linear regression for automatically predicting CVSS base vector. The comparison results suggested that both BERT and MLR perform better than LR, with distinct advantages. BERT excels in understanding context, making it suitable for predicting general CVSS base vector, while MLR is effective for targeting specific attributes or severity levels. Consequently, these methods hold promise in aiding security engineers to promptly address vulnerabilities.

Processes of some business or life activities are sometimes changed due to some reasons, such as the emergence of new technologies and the change of the human behavior caused by a seasonal event, e.g. Christmas. Such changes are called concept drift. Detecting concept drift is useful for many reasons. For example, we can update existing out-of-date business rules. Many methods of concept drift detection in processes have been already proposed. However, most of them are a little bit complex because sliding widows should be defined on a log of business process during its analysis. We thus propose a light-weight method for its detection by using heuristic miner, which is a famous algorithm for process discovery. In our method, we simple observe the discovered model to identify the infrequent actions and transitions between actions. Our method helps us to identify several types of concept drift although some types cannot be detected. We discuss how to overcome current limitations of our method.

The UML (Unified Modeling Language) state machine diagram notation is challenging for learners to understand because of its complexity. Therefore, educators assign modeling assignments to learners to assess their understanding. If learners do not fully understand the notation, they may make errors in their diagrams. To improve learners’ understanding, educators provide the learners with explanations of what and why the diagrams unmet the requirements of the modeling assignments. However, the variety of content and layout in Learner-created diagrams can be challenging for educators to accurately and quickly identify the unmet requirements in each diagram. Therefore, this study proposes a method to semi-automatically identify and measure unmet requirements in Learner-created diagrams. The proposed method was applied to 38 state machine diagrams created by learners to evaluate its effectiveness. Consequently, the proposed method gave reasonable results for 37 out of 38 diagrams (approximately 97%).

Many different activities are performed simultaneously in the real world, and one of them contains actions, that can be utilized in another activity. If such actions are actually utilized, the activity utilizing them become more efficiently than ever. We call the activity providing such actions a contributable activity, and the one utilizing the actions a contributed activity. Our research goal is to find such contribution relationships between activities. To achieve the goal, we used a conformance checking technique in the field of process mining research. In the process mining research, logs of actions and control flow models are used, and the logs and the models are usually represented by the verb attribute in an action such as “submit”, “decide” and so on. However, we could not find some contribution relationships by using the logs and models of the verb attribute. We thus examine the usage of non-verb attributes such as a place or a tool in an action for our goal. Through a case study about elderly people care, we find non-verb attributes have some potential to achieve our goal more comprehensively than ever.

In this study, we propose a method for applying a temporal relation identification model to functional requirements. We discuss the limited availability of data in the requirements engineering domain compared to other fields when used for supervised learning, and therefore employ a corpus from the news domain for training. The experimental results demonstrate that the types of temporal relations present in functional requirements are limited, indicating that focusing on learning with a narrowed set of labels is effective. Additionally, We incorporate Dependency Path (DP) into the temporal relation identification model and report, through comparative experiments, that leveraging DP is effective, but minor modifications to DP do not lead to significant improvements in accuracy. By demonstrating specific application methods of temporal relation identification in requirements engineering, we anticipate contributing to the analysis of functional requirements in software development.

In this study, we consider projects for developing service systems using machine learning (ML) techniques. As ML techniques have been introduced in various domains, there is reusable knowledge on ML projects that can be employed for conducting such projects without facing major failures. The usage of such knowledge during a project has not yet been clearly described in the form of reusable knowledge such as best practices or patterns. Thus, in this study, we propose a method for collecting the ominous signs in ML projects as “bad smells” and incorporating them as a part of such reusable knowledge. We confirmed the effectiveness of the proposed method through an evaluation.

In software development, ambiguities in requirements described in natural language (NL) prevent the application of formal approaches, posing a difficulty that has heretofore been avoided in two main ways: discovery based on formal specifications generated from NL requirements, and the creation of non-ambiguous NL requirements. In the former, NL is more expressive and does not rely on the user‘s expertise, but instead makes the automatic generation of formal specifications difficult. The latter facilitates the automatic generation of formal specifications and has the advantage of reduced syntactic complexity, but in exchange for reduced expressiveness of NL. In this paper, we take an approach that allows users to describe highly expressive NL requirements and reduces syntactic complexity to support the automatic generation of formal specifications from NL requirements. We also propose an information extraction method using syntactic patterns of low syntactic complexity. Applying our method to practical requirement sentences reveals that it is effective in reducing the complexity of information extraction rules. We expect that our method can support the automatic generation of formal specifications from NL requirements without compromising the expressive power of the language.

We propose a method to comprehensively find a pair of business or life activities, which can mutually contribute to each other. We assume that the activities with synchronized processes can mutually contribute to each other. A process model of an activity is thus compared to a log of another activity for measuring whether the activities can synchronize their processes to a certain extent using a conformance checking technique. The threshold of the extent is defined on the basis of a randomly generated log containing the same events as those in the compared log. We tentatively evaluated the method through a case study, and confirmed that the method seemed to be valid.

Model checking with a time aspect is often used in verification on hardware and embedded systems. Timed automata are often used for such models. UPPAAL is a world-wide famous model checking tool for timed automata; however, UPPAAL is a Computational Tree Logic (CTL)-based model checking tool and cannot use Linear Temporal Logic (LTL) properties. Bounded model checking uses LTL (Linear Time Logic) for a checking formula. Bounded model checking specifies a boundary k and obtains counterexamples by searching from the initial state of a system to states reachable by k-steps. There are several studies on bounded model checking. Sorea has proposed a concrete algorithm for a timed automaton. There are, however, no clear details on how to implement bounded model checking tools for timed automata, and study the performance. Another problem is that the timed automaton covered by the method does not support general variables except for clock variables. The objective of this study is to implement a bounded model checking tool using LTL for timed automata. We also improve Sorea's method so that it can handle extended timed automata that handle general variables. This paper also presents some LTL examples from texts on requirement specifications for embedded systems and the results of applying the tool to them.

A requirement specification for software is usually described in a natural language and thus may include sentences containing ambiguity and contradiction. Design errors due to ambiguous expressions and contradictions are often found later in the development process. These ambiguous sentences would force the developer to go back to the design process again. In order to prevent this kind of rework, a method of automatically converting a required specification written in Japanese to a state transition model is desired to help detect ambiguity and contradiction points of the specification. In previous studies, we have improved the extraction accuracy by refining the parsing rules. However, in order to parse a variety of sentences, it is necessary to set up new rules or refine the rules. In this paper, we propose a method of using a dependency analyzer in syntactic parsing. Using dependency analyzers eliminates the need for both of rule setting and refinement. Thus, the disadvantage would be eliminated. In addition, the effectiveness of the proposed method is reported by extracting and showing the elements necessary to create a state transition diagram.

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.

In recent years, information systems have become large-scale and complicated. The demand for research on the cause analysis of information systems that can fail and the construction of countermeasures has been increasing. Systems Theoretic Accident Model and Processes (STAMP) is an accident model based on systems theory. STAMP has the feature that it can analyze not only failures of system components and human errors but also hazards caused by unsafe interaction between components and between components and humans. An analysis method based on the STAMP model System-Theoretic Process Analysis (STPA) is a method for analyzing in advance the possibility of a system accident for the interaction between a controller and a controlled device. As a preliminary study, we have performed STAMP/STPA to ‘Fallen Barrier Trap at Railroad Crossing,” which is an example of STAMP/STPA analysis. We have analyzed with the time automaton model checker UPPAAL in cooperation with STAMP Workbench, a STAMP/STPA support tool. From the preliminary study, we consider that the analysis procedure can be automated in order to reduce the burden on analysts. In this paper, we propose a method for deriving hazard transition sequences as a STAMP/STPA support method. Specifically, the model definition is described in STAMP Workbench, the method of deriving files that can be used in UPPAAL from the definition statement is described, and in order to automate model checking, the SAT/SMT solver is used. The constraints for the clock variables in each component are automatically derived. This paper describes a method for deriving the shortest path as a counterexample that does not satisfy the safety constraint equation by using the calculation method of the concrete value the constraints of and the binary search method. The proposed method is applied manually as the evaluation experiment. We confirmed that an effective sequence corresponding to some hazard scenarios derived by the conventional procedure could be derived.

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.

In recent years, information systems have become large and complicated, and demand for research on accident analysis of such a system and its countermeasure construction is increasing. As an accident model based on system theory, Systems Theoretic Accident Model and Processes (STAMP) has attracted many attention. In STAMP, it is not limited to malfunctions of system components and human errors, but also has feature of possibility to analyze errors of interaction among constituent elements and interaction between constituent elements and human beings. System Theoretical Process Analysis (STPA) is a method for analyzing in advance the possibility of system accident against the interaction between the controller and the controlee. More effective accident analysis can be expected by cooperation of STAMP/SPTA and model checking based on formal method. In this paper, we describe a result of STAMP analysis example of “Fallen Barrier Trap at Railroad Crossing” with automaton model checker UPPAAL. In addition, we consider an automatic detection approach between the STAMP/STPA tool STAMP Workbench and the model checker UPPAAL.

CPS (Cyber Physical System) includes a complicated architecture that spans cyber space and physical space. There are several relationships related to the transmission of data and energy and the control and monitoring of CPS components. Many control loops exist, and these control loops represent the cyclic relations of components based on the relationships mentioned above. Design faults in the control loops can cause accidents, and so we propose a method of verifying the stability of control loops to ensure safety in CPS. In the proposed method, control loops are identified in an architecture model proposed by us. An identified control loop is converted into a temporal logic formula, and the stability of a control loop is verified using a model checking tool. The effectiveness of the proposed method was confirmed by applying a farm monitoring system, which can be considered a type of actual CPS.

The recovery of traceability links to requirements from a functional model created through analysis/design is crucial to understand existing systems for reuse, improvement or maintenance. Functional and non-functional requirements generally are implicitly interpreted and non-systematically woven into a functional model by analysts/designers. Traditional traceability link recovery methods focusing on terminology or syntactic structure, however, have a recovery limit because such interpretation and weave are not paid enough attention. This paper presents a novel idea to decompose such a functional model into model components in order to accurately trace a components to requirements especially non-functional requirements. We call such the decomposition traceability link mining. A screen transition model is adopted as the functional model because of the focus on usability.

This paper presents SMart-Learning, which is a set of state machine simulators for developing thinking skills. SMart-Learning handles a variant state machine diagram notation based on UML. The learners of the diagram require various thinking skills such as requirements analysis, concept formation including abstraction for a domain, and modeling conforming to the semantics. Evaluation of their diagrams is crucial in such learning but should not place an unnecessary burden on the learners when they use tools supporting the evaluation. However, usability aspects other than effectiveness of such tools has not received much attention. SMart-Learning provides three simulators to improve learners' skills step by step. Through an evaluation in which five learners use SMart-Learning, the effectiveness, especially usability, is discussed.

IoT devices are software-rich and Java is sometimes chosen as the developing programming language. Although Java is highly productive in constructing large advanced programs, application or user-defined Java classes must be responsible for safety and security issues. In particular, two fundamental methods hashCode and equals play key roles in safety and security assurance. Some existing studies for ensuring the correctness of these two methods rely on static analysis, which are limited to loop-free programs only. This paper proposes a new solution to this important problem, based on equivalence checking of methods or functions. The proposed approach makes use of software analysis workbench (SAW), an open source tool. The approach is also useful in reducing the cost of regression testing when program refactoring is conducted.

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.

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.

A socio-technical system (STS) consists of many different actors such as people, organizations, software applications and infrastructures. We call actors except both people and organizations machines. Machines should be carefully introduced into the STS because the machines are beneficial to some people or organization but harmful to others. We thus propose a goal-oriented requirements modelling language called GDMA based on i* so that machines with the following characteristics can be systematically specified. First, machines make the goals of each people be achieved more and better than ever. Second, machines make people achieve goals fewer and easier than ever. We also propose analysis techniques of GDMA to judge whether or not the introduction of machines are appropriate or not. Several machines are introduced into an as-is model of GDMA locally with the help of model transformation techniques. Then, such an introduction is evaluated globally on the basis of metrics derived from the model structure. We confirmed that GDMA could evaluate the successful and failure of existing projects.

The purpose of this study is to explore educational methods for conceptual modeling for novices. In this research, the subjects are mainly freshmen in university. Model driven development (MDD) and a domain specific language (DSL) are key factors in this study. By using MDD, learners are expected to be able to evaluate their own model by observing the target device's behavior. By using a DSL, teachers can control the difficulty of the problems given to their learners. In this paper, we describe our research approach using MDD and a DSL, then, show our experiment design and results. We also discuss the effectiveness of MDD in university freshmen courses with proposed educational methodology.

In the requirements definition phase, conceptual models are used to understand the developing software. Although scenarios are often described on the basis of conceptual models, there are cases that necessary requirements are omitted in the scenarios when the scenarios are created manually. Herein we propose an approach to support scenario creation from conceptual models where event lists of scenarios, which include checkpoints to define requirements, are generated from conceptual models automatically. The conceptual models represent the core resources of the software, the owner of the core resources, and use cases as class diagrams. Then software engineers and their clients arrange the event lists and define requirements as scenarios on the basis of the checkpoints. Our approach can support describing scenarios with all the necessary requirements from conceptual models. To confirm the effectiveness of our approach, we compared our approach to the all-manual approach.

When information systems are introduced in a social setting such as a business, the systems will give bad and good impacts on stakeholders in the setting. Requirements analysts have to predict such impacts in advance because stakeholders cannot decide whether the systems are really suitable for them without such prediction. In this paper, we propose a method based on model transformation patterns for introducing suitable information systems. We use metrics of a model to predict whether a system introduction is suitable for a social setting. Through a case study, we show our method can avoid an introduction of a system, which was actually bad for some stakeholders. In the case study, we use a strategic dependency model in i* to specify the model of systems and stakeholders, and attributed graph grammar for model transformation. We focus on the responsibility and the satisfaction of stakeholders as the criteria for suitability about systems introduction in this case study.

A key to success of high quality software development is to define valid and feasible requirements specification. This paper proposes an efficient test case generation method from the validatable UML-based requirements analysis model to improve feasibility of the requirements specification.

This paper proposes the stepwise automatic generation of a prototype system from the scenario based requirements analysis model based on UML. The system is a non-functional user interface prototype for Web applications. A scenarios is defined for every actor and for every normal, alternative and exceptional flow in a use case, so that it enriches the prototype system and is also utilized to test the final product.