Prior research has demonstrated that Artificial Intelligence (AI) models can memorize specific training data, which can then be extracted through targeted attacks. Furthermore, AI's inherent ability to integrate information allows for the aggregation of disparate data points from diverse sources, creating significant privacy vulnerabilities. Conversely, existing Privacy-Enhancing Technologies (PETs) treat the entire dataset or individual Personally Identifiable Information (PII) as uniformly sensitive, neglecting the inherent relationships and interactions between data elements. To address this gap, we propose that data should be classified as sensitive only when a sufficient quantity of such information pertaining to an individual is present within the data. To this end, this study employs knowledge graphs to model private information, creating a hierarchical structured representation, termed PrivGraph, that reveals sensitive data aggregation within training datasets. Leveraging PrivGraph, we introduce the Sensitive Level Factor (SLF) to quantify the degree to which an individual's private information is embedded in the data. Furthermore, we propose PrivGraph-based knowledge probing to facilitate posttraining privacy assessments. Our experiments demonstrated that PrivGraph achieves comparable performance to existing models in private information detection, while also effectively modeling individual private information, even with lengthy texts and data from diverse sources. At the end of the paper, we discuss how PrivGraph can be incorporated throughout the AI engineering lifecycle to provide traceable as well as comprehensive privacy protection. Our code is available at https://github.com/pphui8/PrivGraph.

Many industrial systems that are developed without proper engineering guidance due to a lack of expertise or resources suffer from failures and maintenance problems in their evolving lifecycle. For mission-critical systems, in particular, ensuring high quality of non-functional requirements in a rapidly changing domain environment is of the utmost importance. In this paper, we report our case study with an industry system, a sensor-based earthquake warning system that was developed without a rigorous engineering process. Therefore, no requirements documents are available for future maintenance and verification. In this study, we used various types of software analysis methods such as stakeholder interviews, document reviews, source code analysis, model checking, and software testing for discovering requirements and also for verification purposes. We used a goal modeling approach to gather a set of initial requirements as though they had been elicited using an appropriate requirements engineering method in the early stage of the development process. Furthermore, software testing and model checking were iteratively used to verify and clarify unknown-source, uncertain, and unconfirmed requirements during the revision of the goal model. This study also provides an architectural improvement of the system through the discovery of requirements conflicts and violations. The experience and findings of this study, which demonstrate the effectiveness of applying diverse software engineering techniques in maintenance, can contribute to the analysis and evolution of systems developed without a proper engineering process, by discovering and verifying some critical requirements specifications.

Advanced Persistent Threat (APT) attacks are complex, employing diverse attack elements and increasingly intelligent techniques. This paper introduces a tool for security risk assessment specifically designed for these attacks. This tool assists security teams in systematically analyzing APT attacks to derive adaptive security requirements for mission-critical target systems. Additionally, the tool facilitates the assessment of security risks, providing a comprehensive understanding of their impact on target systems. By leveraging this tool, security teams can enhance defense strategies, mitigating potential threats and ensuring the security of target systems.

In a socio-technically connected environment, self-adaptive systems need to cooperate with others to collect information to provide context-dependent functionalities to users. A key component of ensuring safe and secure cooperation is finding trustworthy information and its providers. Trust is an emerging quality attribute that represents the level of belief in the cooperative environments and serves as a promising solution in this regard. In this research, we will focus on analyzing trust characteristics and defining trust-aware models through the trust-aware goal model and the provenance model. The trust-aware goal model is designed to represent the trust-related requirements and their relationships. The provenance model is analyzed as trust evidence to be used for the trust evaluation. The proposed approach contributes to build a comprehensive understanding of trust and design a trust-aware self-adaptive system. In order to show the feasibility of the proposed approach, we will conduct a case study with the crowd navigation system for an unmanned vehicle system.

With the significant increase of various cyber threats, the strategies of the attacks are becoming more diverse. In particular, more attention needs to be paid to Advanced Persistent Threat (APT) attacks since these attacks are continuously performed on a specific target for an apparent purpose through numerous tactics and techniques without being discovered for a long time. Although it is difficult to detect and respond to such APT attacks, it is more challenging to elicit security requirements that sufficiently reflect these complex characteristics for proactive defense. To address this problem, we propose a tool that recommends security requirements for APT attacks using the Case-Based Problem Domain Ontology specialized for APT attacks.

As one of the most important concepts in the software engineering process, requirements engineering plays an important task in sustainability engineering by understanding the nature of software system and their impacts on the entire dimension of sustainable development. Unfortunately, the process for incorporating sustainability concerns is not a trivial task. Sustainability is a concept with a high level of abstraction and is mostly treated as an afterthought. This work discusses a practical approach on how to capture requirements with sustainability concern related to software development. A stepwise guideline is presented to ease the requirements engineering process that can address sustainability issues. The proposed guideline combined well-known approaches such as the goal-scenario-based approach, analytical hierarchical approach, and feature modeling to capture sustainability requirements as an integrated framework. altogether, this tutorial shows a practical methodology in which the engineer can see which aspects or features that they need to improve to meet the required sustainability indicator and baseline.

Software sustainability evaluation has become an essential component of software engineering (SE) owing to sustainability considerations that must be incorporated into software development. Several studies have been performed to address the issues associated with sustainability concerns in the SE process. However, current practices extensively rely on participant experiences to evaluate sustainability achievement. Moreover, there exist limited quantifiable methods for supporting software sustainability evaluation. Our primary objective is to present a methodology that can assist software engineers in evaluating a software system based on well-defined sustainability metrics and measurements. We propose a novel approach that combines machine learning (ML) and software analysis methods. To simplify the application of the proposed approach, we present a semi-automated tool that supports engineers in assessing the sustainability achievement of a software system. The results of our study demonstrate that the proposed approach determines sustainability criteria and defines sustainability achievement in terms of a traceable matrix. Our theoretical evaluation and empirical study demonstrate that the proposed support tool can help engineers identify sustainability limitations in a particular feature of a software system. Our semi-automated tool can identify features that must be revised to enhance sustainability achievement.

Abstract Due to the critical role of a software‐intensive system in society, software engineers have a responsibility to add sustainability as a goal while developing a software system. However, there is a lack of practical guidelines providing a tangible decomposition of the sustainability aspect. Moreover, there are limited quantifiable methods to support sustainable design and analysis. Therefore, we propose a systematic approach that allows software practitioners to accommodate sustainability concerns that are aligned with other software quality attributes to enhance sustainable development. By using the combination of a goal‐scenario‐based approach and feature modeling, sustainability requirements are elicited along with their functional compositions. Our approach is a comprehensive reference model that decomposes sustainability requirements, enabling analysis, support, and assessment of sustainability risk analysis and guiding the designer to construct a feature model as a system functional decomposition. From the conducted theoretical evaluation and empirical study, our proposed approach can derive more significant sustainability‐related requirements and key features by providing a practical guideline with the integration of well‐known methods to address sustainability in requirements engineering. With the help of the provided approach, we can solve more conflicting goals in different sustainability dimensions.

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

The self-adaptive systems are the systems that can adjust its behavior based on the dynamically changing environments to provide the appropriate services to the user. However, as the user demands the complicated service from the self-adaptive systems, they should cooperate with other systems because they have the dedicated services for the specific demand. The system should select the appropriate cooperation partner among many candidates. In this situation, the trust becomes one of the important criteria to achieve the minimum level of security in the open and distributed environment. However, many existing works on the self-adaptive system focus on the internal adaptation process or the adaptation in the closed environment. As the first attempt to tackle the trust attribute, in this paper, we propose the trust-aware goal modeling from the use case for cooperative self-adaptive system. By analyzing the characteristics of the trust in the requirements engineering process, we can understand when the trust should be considered and how it can be represented in the system design. In addition, we illustrate the unmanned vehicle scenario to show how the proposed approach can be applied to the real case.

As the system is deployed in the dynamic environments, the need of self-adaptive system is increasing. In addition, by advancing the network technology, self-adaptive system interacts with other systems to achieve the complex goals, which are difficult to be achieved by single self-adaptive system. During the interaction, they may share security-critical information. As a result, security becomes an important quality attribute of such systems. Especially, comparing with the traditional security approaches, trust is considered as the soft security, which is more light and casual approach. Trust identifies and prevents the security issues with the intuitive and social mechanism. However, there is no major research on the importance of trust evaluation for the self-adaptive systems. In this paper, we systematically survey the trust evaluation studies focusing on the relevance of the self-adaptive systems. Based on the analyzed results, we suggest the future research directions.

Socio-technical Systems (STS) consist of complicated requirements that consider a variety of stakeholders' viewpoints, and are inherently complex due to heterogeneity characteristics of STS components. However, security in STS is still a major issue, which can be explained by the resulting cost and the impact of the STS intrusion on the whole enterprise. However, research related to recommending security requirements for a target STS is insufficient. Firstly, systematic acquisition of understanding the problem with rich context-awareness is not provided to STS, since the knowledge for the development and execution of STS is scattered. Secondly, the majority of security analysis focuses on only the technical approach, although it is necessary to perform a holistic analysis of STS due to heterogeneity characteristics. In order to solve these problems, we conduct a study of the three-layered framework for recommending security requirements through goal-oriented risk assessment using a Problem Domain Ontology (PDO). By using this framework, we demonstrate how the PDO is built through collecting, analyzing, and categorizing different information and knowledge from various sources, and how security requirements are recommended from the threat analysis and the goal-oriented risk assessment based on PDO. In addition, we discuss the applicability of this framework with a case study based on a real threat scenario. This paper contributes to security requirements engineering research by proposing a methodology for systematically organizing knowledge with a security requirements recommendation framework using the PDO.

The growing complexity and dynamics of the execution environment have been major motivation for designing self-adaptive systems. Although significant work can be found in the field of formalizing or modeling the requirements of adaptive system, not enough attention has been paid towards the requirements elicitation techniques for the same. It is still an open challenge to elicit the users' requirements in the light of various contexts and introduce the required flexibility in the system's behavior at an early phase of requirements engineering. We explore the idea of using a cognitive technique, repertory grid, to acquire the knowledge of various stakeholders along multiple dimensions of problem space and design space. We aim at discovering the scope of variations in the features of the system by capturing the intentional and technical variability in the problem space and design space respectively. A stepwise methodology for finding the right set of features in the changing context has also been provided in this work. We evaluate the proposed idea by a preliminary case study using smart home system domain.

A self-adaptive system reacts to the changing environment by modifying its functionality in relation to the encountered state of the environment.In order to adapt to a new situation, such system goes through many decision points during the adaptation process.Knowledge forms the basis of decision making within the adaptation process.There are already many existing self-adaptive system frameworks.However, these frameworks have limitation in the way they represent the rationale for adaptation and the semantics behind the knowledge they use.This paper takes a step forward by proposing a knowledge-intensive adaptation framework to both manage knowledge and support the analytical decision making process.The proposed approach represents the adaptation knowledge by using ontology which helps to organize, analyze and extend knowledge.Ontology is able to represent the semantics behind knowledge and provide the evidence for the adaptation.The proposed approach uses a special ontology named the Adaptation Problem Domain Ontology.It specifies the system goals, features, architectures, and the relationship between them.This ontology is used to answer the problem of adaptation at each decision point and determine the appropriate system structure by reasoning the semantics behind knowledge.Thus, the system can consider the semantics behind knowledge for adaptation, and then the stakeholders can understand the adaptation process.We apply the proposed framework to the smart grid domain and show how the system adapts to a new situation using rationale for adaptation and the semantics behind the knowledge.

National happiness has been actively studied during last ten years.The factor of happiness could be different due to different human perspective.The factors used in this work include both physical needs and the mental needs of humanity such as educational factor.This work identified more than 90 features that can be used to predict the country happiness.Unfortunately, manually analyzing the features is difficult and needs a lot of resources.Due to numerous size of the features, it is unwise to rely on the prediction of national happiness by manual analysis.That process will result in the high cost of analysis.Therefore, this work used machine learning technique which is a Support Vector Machine to learn and predicts the country happiness.Dimensionality reduction is also done in this work. Using the information gain technique, the features can be reduced.This technique is chosen due to its ability to explore the interrelationships among a set of variables.The selected features are also evaluated using the SVM classifier.Using the data of 187 countries from the UN Development Project, this work is able to identify which factor needed to be improved by a certain country to increase the happiness of their citizens.

National happiness has been actively studied throughout the past years. The happiness factor varies due to different human perspectives. The factors used in this work include both physical needs and the mental needs of humanity, for example, the educational factor. This work identified more than 90 features that can be used to predict the country happiness. Due to numerous features, it is unwise to rely on the prediction of national happiness by manual analysis. Therefore, this work used a machine learning technique called Support Vector Machine (SVM) to learn and predict the country happiness. In order to improve the prediction accuracy, dimensionality reduction technique which is the information gain was also used in this work. This technique was chosen due to its ability to explore the interrelationships among a set of variables. Using data of 187 countries from the UN Development Project, this work is able to identify which factor needed to be improved by a certain country to increase the happiness of their citizens.

The 7th edition of the SESS workshop aims at providing a venue for software engineers and security researchers to exchange ideas and techniques. In fact, software is at core of most of the business transactions and its smart integration in an industrial setting may be the competitive advantage even when the core competence is outside the ICT field. As a result, the revenues of a firm depend directly on several complex software-based systems. Thus, stakeholders and users should be able to trust these systems to provide data and elaborations with a degree of confidentiality, integrity, and availability compatible with their needs. Moreover, the pervasiveness of software products in the creation of critical infrastructures has raised the value of trustworthiness and new efforts should be dedicated to achieve it. However, nowadays almost every application has some kind of security requirement even if its use is not to be considered critical.

Adequate requirements validation could prevent errors from propagating into later development phase, and eventually improve the quality of software systems. However, often validating textual requirements is difficult and error prone. We develop a feedback-based requirements validation methodology that provides an interactive and systematic way to validate a requirements model. Our approach is based on the notion of querying a model, which is built from a requirements specification, with scenario questions, in order to determine whether the model's behavior satisfies the given requirements. To investigate feasibility of our approach, we implemented a Scenario Question Query Engine (SQ2E), which uses scenario questions to query a model, and performed a preliminary case study using a real-world application. The results show that the approach we proposed was effective in detecting both expected and unexpected behaviors in a model. We believe that our approach could improve the quality of requirements and ultimately the quality of software systems.

This article presents a novel method for modeling swept volumes of milling tools that undergoes a translational motion by extending the basic idea behind Gauss maps. The main idea is that it 1) transforms the tool shape and the moving direction of the tool from Euclidean space into an unit sphere by means of Gauss function and gets the spherical domains which are called Tool map (T-Map) and Contact map (C-Map) respectively 2) finds an intersection region of T-Map with C-Map and 3) transforms intersected region inversely into Euclidean space to get the envelope profile of the tool at prescribed instant time. The proposed approach is intuitive to understand the cutting behavior of generalized tool shape, simple to implement the swept volume and extendible up to a piecewise C -continuous convex set following translational motion.

Detection technology is a requirement for an Augmented Reality system. One of the problems with detection technology is the instability problem, which occurs when an obstacle occludes a tag while detecting the tag, and the augmented object suddenly disappears. We have proposed a corner detection algorithm to solve this instability problem. The key feature is that if the tag can recognize its position using its four corner cells despite the obstacle being present, then it can maintain its augmented object. We defined the corner case for all types of cases where the instability problem occurs in ARToolkit or ARTag. We have adapted our proposed algorithm to the corner case in ARToolkit, ARTag and ColorCode vision systems and have compared their false detection rates.