Formal semantics and verification for feature modeling

The paper examined the problem of modeling and design of software computer systems. The article briefly describes the content aspects of the various stages of the implementation of software complexes and systems. The methodology of research and analysis of systems, which uses the method of computer simulation modeling, is given. The aim of the work is to create a new universal approach to the design and implementation of software systems of various levels of complexity. During the study, the following general scientific methods were used: system analysis, synthesis, formalization, modeling, decomposition, structuring, description, comparison. The materials used in the study were: computers and software development tools. As a result of the study proposed a universal agent-module approach to the design and implementation of software systems based on simulation agent-based modeling. The study presents the processes of modeling and designing software systems, their stages, and the possibility of their integration within the described approach. Examples of formal model and structure of the software system under design based on the agent-module approach are presented. The proposed approach can be used in the process of software development at the initial stages related to modeling and designing of software systems. Integration of modeling and design stages in the proposed approach will significantly reduce software development time and simplify the design process by making it clearer and more formalized. Materials of the article are of scientific and practical value for a wide range of specialists interested in system analysis, modeling, design and implementation of software systems.

Background: People with disabilities have experienced heightened social risks in the context of the pandemic, resulting in higher rates of infection and mortality. They have also borne elevated burdens associated with public health measures. The United Nations Convention on the Rights of Persons with Disabilities (UNCRPD) obliges its 184 state parties to eliminate discrimination and ensure equality and inclusion for persons with disabilities, including protection and safety in situations of emergency. It remains unclear to what extent national COVID-19 policies have aligned with these commitments under the UNCRPD. Our objective in this exploratory study was to assess alignment between the UNCRPD indicators and COVID-19 policies from 14 countries with the goal of informing policy development that is inclusive of persons with disabilities and responsive to rights under the UNCRPD.Methods: We identified COVID-19 policy documents from 14 purposively selected countries. Country selection considered diversity based on geographic regions and national income levels, with restriction to those countries that had ratified the UNCRPD and had English or French as an official language. We used a computational text mining approach and developed a complex multilevel dictionary or categorization model based on the UNCRPD Bridging the Gap indicators proposed by the Office of the High Commissioner on Human Rights (OHCHR). This dictionary was used to assess the extent to which indicators across the entirety of the UNCRPD were represented in the selected policies. We analyzed frequency of associations with UNCRPD, as well as conducting ‘key word in context’ analyses to identify themes. Results: We identified 764 COVID-19 national policy documents from the period of January 2020 to June 2021. When analyzed in relation to the Articles of the UNCRPD, the most frequently identified were Articles 11 (risk and humanitarian emergencies), 23 (home and family), 24 (education), and 19 (community living). Six countries produced 27 policies that were specifically focused on disability. Common themes within these documents included continuation of services, intersectionality and equity, and disability considerations in regulations and public health measures.Conclusion: Analyzing country policies in light of the UNCRPD offers important insights about how these policies do and do not align with states’ commitments. As new policies are developed and existing ones revised, more comprehensive approaches to addressing the rights of persons with disabilities are urgently needed.

The feature model considers several products as a collection of features and describes the commonality and variability of the family in terms of feature relationships. Traditional informal feature models lacked the support of tools to automate the process of feature modeling, so formal methods were introduced implement automated support for feature modeling tools. For large software, writing formal specifications for feature models directly is quite difficult and requires technical background and experience. In this paper, we propose a formal template-based approach to constructing feature models and their formal descriptions, each template is used to formalize a class of PFA expressions. We give a structural definition of the templates and for constructing feature models from templates and generating formal descriptions of the corresponding features. We give a case study to illustrate the effectiveness of our method.

In software architecture design, we explore design alternatives and make decisions about adoption or rejection of a design from a web of complex and often uncertain information. Different architectural design decisions may lead to systems that satisfy the same set of functional requirements but differ in certain quality attributes. In this paper, we propose a Bayesian Network based approach to rational architectural design. Our Bayesian Network helps software architects record and make design decisions. We can perform both qualitative and quantitative analysis over the Bayesian Network to understand how the design decisions influence system quality attributes, and to reason about rational design decisions. We use the KWIC (Key Word In Context) example to illustrate the principles of our approach.

If we model a family of software applications with a feature model and an architecture model, we are describing the same subject from different perspectives. Hence, we are running the risk of inconsistencies. For instance, the feature model might allow feature configurations that are not realizable by the architecture.In this paper we tackle this problem by providing a formalization of dependencies between features and components. Further, we demonstrate that this formalization offers a better understanding of the modeled concepts. Moreover, we propose automated techniques that derive additional information and provide feedback to the user. Finally, we discuss how some of these techniques can be implemented.

This paper reports on interviews conducted during June and July 2015 to document ecological knowledge of beluga whales (Delphinapterus leucas) in Ulukhaktok, Northwest Territories. Beluga whales are not a traditionally available or important species for Ulukhaktomiut, but they have appeared in increasing numbers in the waters around the community, and hunters have actively pursued and taken them. We conducted interviews in English with 31 Inuit about their beluga knowledge. A Key Words in Context (KWIC) analysis of the word “know” in the narratives reveals different conceptions of what it means to know something about whales. “Know” variously references practical skill, concern and empathy for others, or the developing awareness of one’s place in the world. Each of these meanings is coded uniquely in Inuinnaqtun, providing insights about the important differences between researchers and Inuit in how “ecological knowledge” is understood and activated.

Corpora in language teaching is an approach which incorporates corpus data both in and out of the classroom to help students notice and practice the forms, meanings, and use of particular language structures. Learners use authentic language data, often presented in the form of Key Word in Context (KWIC) lines, to act as “linguistic detectives.” Approaches to corpora in the classroom have been around for nearly 30 years, and the tools required to conduct searches have become relatively easy to use. Still this approach has not been widely implemented in the language classroom. It is hoped that both ESL teachers and students will begin to see the value of corpus‐driven learning in language‐teaching/learning processes and begin using these approaches in their ESL classrooms.

The customer oriented individualization of products is getting more and more important for the production industry. Especially in car manufacturing industry we can observe a dramatically increasing number of product variants not only related to different car concepts but also concerning different functionality as for example in car entertainment. In order to cope with this increasing complexity in terms of product features and their interrelationships, manufacturers more and more build on a formal approach called feature modeling that allows for a formal analysis of the specified variability artifacts with the help of specialized proving engines as for example SAT-solvers. The development of such proving engines and their test is quite complicated also due to the fact that manufacturers do not disclose the real development data for understandable reasons. Thus, a framework is needed that enables the proving engine developers to test their engines on nearly real data and to show the potential and possibilities of their engines without having the real development data at hand. This paper presents a framework for generating especially large parameterized feature models used for load testing and benchmarking feature model analysis tools, as well as two usage scenarios: the first one runs a typical benchmark with large feature models on two versions of the theorem prover SPASS, the second shows the integration of the generator in a client-server environment where its functionality is hosted on a website, i.e. using the browser as a frontend working on tablets and modern smartphones.

IntroductionIn August 2017, a cholera outbreak started in Muna Garage Internally Displaced Persons camp, Borno state, Nigeria and >5000 cases occurred in six local government areas. This qualitative study evaluated...

Students with disabilities (SWD), compared to their non-disabled peers, are at a higher risk for high school dropout and academic failure following the middle to high school transition. The purpose of this study was to investigate teachers' understanding of the elements associated with the middle to high school experience for SWD. Guided by Bronfenbrenner's ecological systems theory, this study explored the following research question: How do teachers make sense of their students' ecological transitions, inclusive pedagogy, and stakeholder involvement and its impact on the social emotional and academic development of students with disabilities transitioning from middle to high school? Using case study methodology, interviews were conducted with five Boston special education teachers who had experience in working with ninth grade SWD. The data analysis was conducted in three phases: (1) categorical aggregation with key terms, (2) direct interpretation of participants' experiences, and (3) categorical aggregation of key words in context. Research findings revealed four key themes that provided more insight on the middle to high school transition for SWD: the diverse learners, bridging the gap, educational stakeholder collaboration, and caregiver involvement. The findings also revealed a general understanding on meeting the needs of SWD and suggested participants' limited knowledge on students' ecological systems and how it impacted the academic and social emotional development of adolescent SWD. Future research in special education and the middle to high school transition must consider how the implementation of inclusive practices in school visions and professional practice through stakeholder collaboration can foster a community of continuity and support for SWD during this critical period of transition.--Author's abstract

Feature models are models used to capture differences and commonalities between software features, enabling the representation of variability within software. There are many variations of feature models and different notations are often used to represent the same information. Currently support for validating or integrating feature models is missing. In this paper, we provide an ontology framework for feature modeling which consists of an ontology that formally provides a specification for feature models. In addition, we provide means to integrate segmented feature models and provide a rule based model consistency check and conflict detection. We use SWRL rules to implement the rules and a DL reasoner to evaluate the rules and infer extra interesting information regarding the variability of the software.

Formal verification involves the use of logical and computational methods to establish claims that are expressed in precise mathematical terms. These can include ordinary mathematical theorems, as well as claims that pieces of hardware or software, network protocols, and mechanical and hybrid systems meet their specifications. In practice, there is not a sharp distinction between verifying a piece of mathematics and verifying the correctness of a system: formal verification requires describing hardware and software systems in mathematical terms, at which point establishing claims as to their correctness becomes a form of theorem proving. Conversely, the proof of a mathematical theorem may require a lengthy computation, in which case verifying the truth of the theorem requires verifying that the computation does what it is supposed to do. The gold standard for supporting a mathematical claim is to provide a proof, and twentieth-century developments in logic show most if not all conventional proof methods can be reduced to a small set of axioms and rules in any of a number of foundational systems. With this reduction, there are two ways that a computer can help establish a claim: it can help find a proof in the first place, and it can help verify that a purported proof is correct. Automated theorem proving focuses on the “finding” aspect. Resolution theorem provers, tableau theorem provers, fast satisfiability solvers, and so on provide means of establishing the validity of formulas in propositional and first-order logic. Other systems provide search procedures and decision procedures for specific languages and domains, such as linear or nonlinear expressions over the integers or the real numbers. Architectures like SMT (“satisfiability modulo theories”) combine domain-general search methods with domain-specific procedures. Computer algebra systems and specialized mathematical software packages provide means of carrying out mathematical computations, establishing mathematical bounds, or finding mathematical objects. A calculation can be viewed as a proof as well, and these systems, too, help establish mathematical claims. Automated reasoning systems strive for power and efficiency, often at the expense of guaranteed soundness. Such systems can have bugs, and it can be difficult to ensure that the results they deliver are correct. In contrast, interactive theorem proving focuses on the “verification” aspect of theorem proving, requiring that every claim is supported by a proof in a suitable axiomatic foundation. This sets a very high standard: every rule of inference and every step of a calculation has to be justified by appealing to prior definitions and theorems, all the way down to basic axioms and rules. In fact, most such systems provide fully elaborated “proof objects” that can be communicated to other systems and checked independently. Constructing such proofs typically requires much more input and interaction from users, but it allows us to obtain deeper and more complex proofs. The Lean Theorem Prover aims to bridge the gap between interactive and automated theorem proving, by situating automated tools and methods in a framework that supports user interaction and the construction of fully specified axiomatic proofs. The goal is to support both mathematical reasoning and reasoning about complex systems, and to verify claims in both domains.

Software product lines are able to describe multiple products sharing a common base of features and are commonly described as feature models. For complex software product lines, automatic analyses are required to ensure validity and to improve the interactive configuration process. Modern SAT solvers are vital components for the validation process of feature models. The increasing variability of software product lines implies the need to use more expressive solvers like SMT solvers. To assist the development of feature modeling tools, we compare SAT and SMT solvers for the automated analysis of feature models. During this thesis, we create an abstract data type to formally define analyses for feature model defects and their explanations. The result shows that SAT solvers are more efficient at detecting the defects, while SMT solvers can find explanations for them multiple times faster. Feature models can be further expanded by attaching attributes to features. Such attributes may contain a numerical value. Additionally, one attribute can be defined for multiple features. In this thesis, we aim to support the interactive configuration process, by providing the range of the sum of values for an attribute. Such ranges depend on the remaining choices in a configuration of the product line. We provide an exact computation using SMT and an approximation using a heuristic. The evaluation results show that an SMT solver is not suitable for supporting interactive configuration. However, the approximated ranges of the provided heuristic were very close to the exact ones.

This paper showcases how a do-it-yourself (DIY) corpus on the United Nations’ Sustainable Development Goals (SDGs) can be used to increase corpus users’ language awareness (at word, phrase, sentence, and discourse levels) and criticality. To this end, the author justifies the close link between corpus linguistics, various critical approaches, and SDGs in the applied linguistics/ELT literature. Subsequently, the author provides an overview of principles for creating a specialized DIY corpus containing around 700,000 words/tokens based on 882 articles of The Jakarta Post from 2012 to mid-2024. In view of critical discourse analysis in corpus linguistics, SDG-related key word(s) in context (KWIC) were analyzed in four stages to examine their (1) frequencies and collocates, (2) concordance lines, (3) larger contexts within or across texts in the corpus (or beyond), and (4) potential to stimulate questions aiming at social transformation. In the findings, the four-stage analyses explore two of the most frequent SDGs in the corpus – “gender” (SDG 5) and “clean water and sanitation” (SDG 6) – to illustrate poststructuralist and Marxist criticality, respectively. The decolonial criticality is demonstrated through a corpus analysis of the word “indigenous” and its collocates. Possible pedagogical applications of the corpus-informed approach are also discussed.

This study investigates the effectiveness of the Corpus of Contemporary American English (COCA) as a teaching tool for enhancing writing skills in English as a Second Language (ESL) learners within a Foundation Program context. The primary research question is: How does COCA influence the writing abilities of ESL students? The aim is to explore COCA’s potential to facilitate a process-based approach to writing, addressing common challenges ESL educators face. This study is significant as it contributes to integrating technology into second language learning through the application of a comprehensive linguistic corpus; COCA features over one billion words and offers various search functionalities, such as List, Chart, Collocates, Compare, Word, and Keyword in Context (KWIC). The research uses qualitative analysis to evaluate COCA’s impact on learners’ writing processes and outcomes. Findings indicate that COCA significantly enhances students’ writing skills across three structured stages: the brainstorming stage for gathering information, the while-writing stage to minimize incorrect vocabulary usage, and the post-writing stage focusing on feedback and assessment. The results demonstrate that employing COCA within a student-centered framework promotes engagement and skill development in writing. The study concludes with recommendations for further research to validate COCA’s effectiveness in teaching other language skills, including grammar, reading, and vocabulary, thereby maximizing its utility in comprehensive English language learning and ensuring that it becomes a vital resource for ESL educators.