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This document is a guest editorial for the special section on SEFM 2020 and 2021 in the journal Software & Systems Modeling. The International Conference on Software Engineering and Formal Methods (SEFM) aims to bring together practitioners and researchers to advance the state of the art in formal methods and their application in the software industry. This special section includes selected papers from SEFM 2020 and 2021, which were held virtually during the COVID pandemic. Each article in this section has undergone rigorous peer review and has been revised and extended compared to its conference version. The selected papers cover topics such as embedded software portability and verification, nontermination inference, quantitative modeling and analysis of BDI agents, lazy model checking for recursive state machines, P-stable abstractions of hybrid systems, fairness and guarantees for extended bounded response LTL+P synthesis, counterexample classification, analyzing the impact of human errors on interactive service robotic scenarios, and active model learning of stochastic reactive systems. [Extracted from the article]

The article discusses the Sustainable Development Goals (SDGs) of the United Nations, which were adopted by all member states in 2015. These goals aim to address pressing global issues such as poverty, health, education, inequality, economic growth, climate change, and environmental preservation. The article suggests that the field of modeling, particularly in computer science and software engineering, can play a significant role in understanding and predicting the impact of changes related to these goals. It emphasizes the need for robust and integrated models that can capture the complexity and interactions of the SDGs, and calls for further research and development in this area. [Extracted from the article]

Building on the computer science concept of code smells, we initiate the study of law smells, i.e., patterns in legal texts that pose threats to the comprehensibility and maintainability of the law. With five intuitive law smells as running examples—namely, duplicated phrase, long element, large reference tree, ambiguous syntax, and natural language obsession—, we develop a comprehensive law smell taxonomy. This taxonomy classifies law smells by when they can be detected, which aspects of law they relate to, and how they can be discovered. We introduce text-based and graph-based methods to identify instances of law smells, confirming their utility in practice using the United States Code as a test case. Our work demonstrates how ideas from software engineering can be leveraged to assess and improve the quality of legal code, thus drawing attention to an understudied area in the intersection of law and computer science and highlighting the potential of computational legal drafting. [ABSTRACT FROM AUTHOR]

This document is a guest editorial for a special section in the journal "Software & Systems Modeling." The special section features revised and extended versions of selected papers from the 20th International Conference on Software Engineering and Formal Methods (SEFM'22), which was held in Berlin, Germany in September 2022. The SEFM conference series aims to advance the state of the art in formal methods and their integration into practical software engineering methods and tools. The special section includes papers on topics such as user-centric analysis, automated generation of finite automata, formal specifications of smart contracts, distributed verification of programs, and abstraction-based verification of neural networks. The authors express their gratitude to the authors and reviewers for their contributions and thank Springer for their support in publishing the special section. [Extracted from the article]

The text is a preface to the Journal of Computer Science & Technology, specifically a special section on the Computer Graphics International (CGI) conference. The CGI conference is a prestigious international academic conference in the field of computer graphics and virtual reality. It aims to foster collaboration between different disciplines and showcase advancements in computer graphics technology. The CGI 2023 conference was held in Shanghai, China, and featured discussions on various topics including computer graphics, virtual reality, geometric design, animation, and healthcare. The special section in the journal includes five selected papers that cover a range of topics and provide an overview of the latest trends in computer graphics and virtual reality. The preface expresses gratitude to the authors and reviewers and encourages readers to explore the special section to stay updated on the developments in these fields. [Extracted from the article]

In recent years, Data Science emerged as a new and important discipline. SOFSEM provides an interesting forum for having and reinforcing the prerequisites of Data Science, coving topics related to Fundamental Computer Science, Data and Knowledge Management, and Software Engineering. [Extracted from the article]

This document is a guest editorial for a special issue of the journal "Programming & Computer Software" titled "Recent Trends on Advanced Computing: The Converging Technologies." The issue features research and practical implementation results from researchers and industry experts in computer science, engineering, and technology. The papers cover a range of topics including microservices, software quality, user engagement on social media, non-functional requirements, medical software architecture, fallacies in political speeches, intelligent learning environments, and more. The guest editors for this special issue are Prof. Dr. Andrei Tchernykh, Prof. Reyes Juárez Ramírez, Dr. Esteban Mocskos, and Prof. Sergio Nesmachnow. [Extracted from the article]

Underrepresentation of women in computer science (CS) increasingly demands the necessity to find and enhance current learning engagement approaches to bring more women into computing fields. Some researchers have been exploring the influence of gamification on female students as one of these possible learning engagement strategies. Gamification refers to the introduction of video game elements into non-game activities to enhance engagement and motivation. Previous studies have reported mixed results of the impact of gamification on women. In this study, we introduce SEP-CyLE (Software Engineering and Programming Cyberlearning Environment), an online gamified tool that was designed to provide supplemental computing content to students. This paper presents a convergent mixed-methods study guided by social identity theory and self-efficacy to understand women's experiences with this gamified tool. More specifically, this study explores virtual points' and leaderboards' effects on CS identity development, self-efficacy, and performance. The results show that virtual points and the leaderboard contributed to improved performance for students of all genders, suggesting that gamification is a gender-neutral learning engagement strategy that improves female students' performance as much as male students. Regardless of improved performance, most women did not actively enjoy or were motivated by the virtual points or leaderboard in SEP-CyLE. Additionally, gamification had no significant impact on CS identity development or self-efficacy constructs and had little to no impact on women's interest and engagement in the field of computing. [ABSTRACT FROM AUTHOR]

Open-science initiatives have gained substantial momentum in computer science, and particularly in software-engineering research. A critical aspect of open-science is the public availability of artifacts (e.g., tools), which facilitates the replication, reproduction, extension, and verification of results. While we experienced that many artifacts are not publicly available, we are not aware of empirical evidence supporting this subjective claim. In this article, we report an empirical study on software artifact papers (SAPs) published at the International Conference on Software Engineering (ICSE), in which we investigated whether and how researchers have published their software artifacts, and whether this had scientific impact. Our dataset comprises 789 ICSE research track papers, including 604 SAPs (76.6 %), from the years 2007 to 2017. While showing a positive trend towards artifact availability, our results are still sobering. Even in 2017, only 58.5 % of the papers that stated to have developed a software artifact made that artifact publicly available. As we did find a small, but statistically significant, positive correlation between linking to artifacts in a paper and its scientific impact in terms of citations, we hope to motivate the research community to share more artifacts. With our insights, we aim to support the advancement of open science by discussing our results in the context of existing initiatives and guidelines. In particular, our findings advocate the need for clearly communicating artifacts and the use of non-commercial, persistent archives to provide replication packages. [ABSTRACT FROM AUTHOR]

Various computing subdisciplines, such as computer science and software engineering, each have their own curricular guidelines. They can be very difficult to understand and compare for people such as prospective students, industry personnel, and even faculty members. This is compounded by a lack of information surrounding undergraduate computing curricular topics via visual methods. This paper describes two experimental activities where the objective is to explore the possibility of obtaining quantitative data sets necessary for visualization, one based on competencies and the other based on knowledge areas. Both activities were based on surveys. The results from the first activity showed that a consensus interpretation could be obtained for the knowledge, skills, and dispositions implied by the competency descriptions, although not as strongly for dispositions. The second activity resulted in a table of knowledge areas with minimum and maximum weights for six computing subdisciplines. Finally, this paper also shows two examples of how users can explore the various curricular guidelines through visualization. [ABSTRACT FROM AUTHOR]

Great software engineers are essential to the creation of great software. However, today, we lack an understanding of what distinguishes great engineers from ordinary ones. We address this knowledge gap by conducting one of the largest mixed-method studies of experienced engineers to date. We surveyed 1,926 expert engineers, including senior engineers, architects, and technical fellows, asking them to judge the importance of a comprehensive set of 54 attributes of great engineers. We then conducted 77 email interviews to interpret our findings and to understand the influence of contextual factors on the ratings. After synthesizing the findings, we believe that the top five distinguishing characteristics of great engineers are writing good code, adjusting behaviors to account for future value and costs, practicing informed decision-making, avoiding making others' jobs harder, and learning continuously. We relate the findings to prior work, and discuss implications for researchers, practitioners, and educators. [ABSTRACT FROM AUTHOR]

A systematic literature review (SLR) is an empirical method used to provide an overview of existing knowledge and to aggregate evidence within a domain. For computer science, several threats to the completeness of such reviews have been identified, leading to recommendations and guidelines on how to improve their quality. However, few studies address to what extent researchers can replicate an SLR. To conduct a replication, researchers have to first understand how the set of primary studies has been identified in the original study, and can ideally retrieve the same set when following the reported protocol. In this article, we focus on this initial step of a replication and report a two-fold empirical study: Initially, we performed a tertiary study using a sample of SLRs in computer science and identified what information that is needed to replicate the searches is reported. Based on the results, we conducted a descriptive, multi-case study on digital libraries to investigate to what extent these allow replications. The results reveal two threats to replications of SLRs: First, while researchers have improved the quality of their reports, relevant details are still missing—we refer to a reporting threat. Second, we found that some digital libraries are inconsistent in their query results—we refer to a searching threat. While researchers conducting a review can only overcome the first threat and the second may not be an issue for all kinds of replications, researchers should be aware of both threats when conducting, reviewing, and building on SLRs. [ABSTRACT FROM AUTHOR]

The current development of the Internet and its growing use make it necessary to satisfy the needs of users with disabilities. The primary objective of this study is to examine healthcare-related websites in nine European countries in order to evaluate the status of their accessibility. Such a detailed statistical comparison has not yet been made in Europe, especially as the present study offers a dual measurement system combining both the application of automated testing software and statistical analysis of user feedback. The study compares 48 websites from Eastern Europe with 51 sites from Western and Northern Europe. The research phase was performed in three steps: firstly by using AChecker, secondly by Nibbler and subsequently followed by user feedback questionnaires evaluated by a group of experts. The overall goal of this study is to determine the most common accessibility problems and to draw site owners' attention to shortcomings so that they can improve the quality of service of their healthcare-related sites in the future. The investigated European websites are grouped into Eastern and Western–Northern countries. We compared our results from different perspectives and ascertained that no significant differences can be established between the two groups predicated on their respective economic situations. Equally, no correlations were observed while comparing the sizes of web pages in Kbytes, the number of barriers and their Nibbler accessibility scores. Furthermore, there appears to be no correlation between the results of the software tests and the percentage of the elderly population in the respective country. [ABSTRACT FROM AUTHOR]

Equal access to all software and digital content should be a reality in the Digital Era. This argument is something defended both by existing regulations, norms and standards, and also business organizations and governments. Despite this acknowledgement, the reality is still far from the desired equality. For certain groups of disabled or impaired citizens, such as the visually impaired, the existence of e-accessibility compliance represents an opportunity to integrate, in a more simple and straightforward manner, their societies. Despite the existing poor results on e-accessibility compliance, the mentioned citizens insist on using digital devices in their daily lives. Even though, in the last decade, multiple standards and regulations have been published towards indicating how to develop accessible digital user interfaces, there are still two major issues surrounding its implementation: the complexity and disparity of the documents containing the abovementioned norms, and also the lack of e-accessibility know-how by software experts. With this in mind, a proposal for an accessible software development model that encompasses e-accessibility incorporation as one of the development process activities has been presented. This model might represent a very interesting support tool for software development organizations and a novel resource for learning and training institutions to be able to improve their computer science and informatics students' skills on e-accessibility. [ABSTRACT FROM AUTHOR]

We present a controlled experiment for the empirical evaluation of example-driven modeling (EDM), an approach that systematically uses examples for model comprehension and domain knowledge transfer. We conducted the experiment with 26 graduate (Masters and Ph.D. level) and undergraduate (Bachelor level) students from electrical and computer engineering, computer science, and software engineering programs at the University of Waterloo. The experiment involves a domain model, with UML class diagrams representing the domain abstractions and UML object diagrams representing examples of using these abstractions. The goal is to provide empirical evidence of the effects of suitable examples on model comprehension, compared to having model abstractions only, by having the participants perform model comprehension tasks. Our results show that EDM is superior to having model abstractions only, with an improvement of 39% for diagram completeness, 33% for questions completeness, 71% for efficiency, and a reduction in the number of mistakes by 80%. We provide qualitative results showing that participants receiving model abstractions augmented with examples experienced lower perceived difficulty in performing the comprehension tasks, higher perceived confidence in their tasks' solutions, and asked 90% fewer clarifying domain questions. We also present participants' feedback regarding the usefulness of the provided examples, their number and types, as well as the use of partial examples. We present a taxonomy of the different types of examples, explain their significance, and propose guidelines for manual and automatic creation of useful examples. [ABSTRACT FROM AUTHOR]

In most areas of computer science (CS), and in the software domain in particular, international conferences are as important as journals as a venue to disseminate research results. This has resulted in the creation of rankings to provide quality assessment of conferences (specially used for academic promotion purposes) like the well-known CORE ranking created by the Computing Research and Education Association of Australasia. In this paper we analyze 102 CORE-ranked conferences in the software area (covering all aspects of software engineering, programming languages, software architectures and the like) included in the DBLP dataset, an online reference for computers science bibliographic information. We define a suite of metrics focusing on the analysis of the co-authorship graph of the conferences, where authors are represented as nodes and co-authorship relationships as edges. Our aim is to first characterize the patterns and structure of the community of researchers in software conferences. We then try to see if these values depend on the quality rank of the conference justifying this way the existence of the different classifications in the CORE-ranking system. [ABSTRACT FROM AUTHOR]

The authors discuss software reverse engineering including its significance in software development and software maintenance, and mention tools of reverse engineering . The authors also discuss the process of code review that help to understand whether or not the codes are evaluated in a timely manner.

I Refutation-based synthesis in SMT i [[8]] introduces the first program synthesis engine implemented inside an SMT solver. I Alloy* a general-purpose higher-order relational constraint solver i [[6]] presents a general-purpose constraint solver that augments syntax-guided synthesis from first order-logics to higher-order logics. While prior work in the area used SMT solvers as a black-box, this work shows the benefits one can gain by accessing the inners of an SMT solver to solve the problem at hand. [Extracted from the article]

Model composition plays a central role in many software engineering activities, e.g., evolving design models to add new features. To support these activities, developers usually rely on model composition heuristics. The problem is that the models to-be-composed usually conflict with each other in several ways and such composition heuristics might be unable to properly deal with all emerging conflicts. Hence, the composed model may bear some syntactic and semantic inconsistencies that should be resolved. As a result, the production of the intended model is an error-prone and effort-consuming task. It is often the case that developers end up examining all parts of the output composed model instead of prioritizing the most critical ones, i.e., those that are likely to be inconsistent with the intended model. Unfortunately, little is known about indicators that help developers (1) to identify which model is more likely to exhibit inconsistencies, and (2) to understand which composed models require more effort to be invested. It is often claimed that software systems remaining stable over time tends to have a lower number of defects and require less effort to be fixed than unstable systems. However, little is known about the effects of software stability in the context of model evolution when supported by composition heuristics. This paper, therefore, presents an exploratory study analyzing stability as an indicator of inconsistency rate and resolution effort on model composition activities. Our findings are derived from 180 compositions performed to evolve design models of three software product lines. Our initial results, supported by statistical tests, also indicate which types of changes led to lower inconsistency rate and lower resolution effort. [ABSTRACT FROM AUTHOR]

Event-based communication is used in different domains including telecommunications, transportation, and business information systems to build scalable distributed systems. Such systems typically have stringent requirements for performance and scalability as they provide business and mission critical services. While the use of event-based communication enables loosely-coupled interactions between components and leads to improved system scalability, it makes it much harder for developers to estimate the system's behavior and performance under load due to the decoupling of components and control flow. In this paper, we present our approach enabling the modeling and performance prediction of event-based systems at the architecture level. Applying a model-to-model transformation, our approach integrates platform-specific performance influences of the underlying middleware while enabling the use of different existing analytical and simulation-based prediction techniques. In summary, the contributions of this paper are: (1) the development of a meta-model for event-based communication at the architecture level, (2) a platform aware model-to-model transformation, and (3) a detailed evaluation of the applicability of our approach based on two representative real-world case studies. The results demonstrate the effectiveness, practicability and accuracy of the proposed modeling and prediction approach. [ABSTRACT FROM AUTHOR]

The accuracy of performance-prediction models is crucial for widespread adoption of performance prediction in industry. One of the essential accuracy-influencing aspects of software systems is the dependence of system behaviour on a configuration, context or history related state of the system, typically reflected with a (persistent) system attribute. Even in the domain of component-based software engineering, the presence of state-reflecting attributes (the so-called internal states) is a natural ingredient of the systems, implying the existence of stateful services, stateful components and stateful systems as such. Currently, there is no consensus on the definition or method to include state-related information in component-based prediction models. Besides the task to identify and localise different types of stateful information across component-based software architecture, the issue is to balance the expressiveness and complexity of prediction models via an effective abstraction of state modelling. In this paper, we identify and classify stateful information in component-based software systems, study the performance impact of the individual state categories, and discuss the costs of their modelling in terms of the increased model size. The observations are formulated into a set of heuristics-guiding software engineers in state modelling. Finally, practical effect of state modelling on software performance is evaluated on a real-world case study, the SPECjms2007 Benchmark. The observed deviation of measurements and predictions was significantly decreased by more precise models of stateful dependencies. [ABSTRACT FROM AUTHOR]

To predict the performance of an application, it is crucial to consider the performance of the underlying infrastructure. Thus, to yield accurate prediction results, performance-relevant properties and behaviour of the infrastructure have to be integrated into performance models. However, capturing these properties is a cumbersome and error-prone task, as it requires carefully engineered measurements and experiments. Existing approaches for creating infrastructure performance models require manual coding of these experiments, or ignore the detailed properties in the models. The contribution of this paper is the Goal-oriented INfrastructure Performance EXperiments ( Ginpex) approach, which introduces goal-oriented and model-based specification and generation of executable performance experiments for automatically detecting and quantifying performance-relevant infrastructure properties. Ginpex provides a metamodel for experiment specification and comes with predefined experiment templates that provide automated experiment execution on the target platform and also automate the evaluation of the experiment results. We evaluate Ginpex using three case studies, where experiments are executed to quantify various infrastructure properties. [ABSTRACT FROM AUTHOR]

Probabilistic models are commonly used to evaluate quality attributes, such as reliability, availability, safety and performance of software-intensive systems. The accuracy of the evaluation results depends on a number of system properties which have to be estimated, such as environmental factors or system usage. Researchers have tackled this problem by including uncertainties in the probabilistic models and solving them analytically or with simulations. The input parameters are commonly assumed to be normally distributed. Accordingly, reporting the mean and variances of the resulting attributes is usually considered sufficient. However, many of the uncertain factors do not follow normal distributions, and analytical methods to derive objective uncertainties become impractical with increasing complexity of the probabilistic models. In this work, we introduce a simulation-based approach which uses Discrete Time Markov Chains and probabilistic model checking to accommodate a diverse set of parameter range distributions. The number of simulation runs automatically regulates to the desired significance level and reports the desired percentiles of the values which ultimately characterises a specific quality attribute of the system. We include a case study which illustrates the flexibility of this approach using the evaluation of several probabilistic properties. [ABSTRACT FROM AUTHOR]