6G will revolutionize the software world allowing faster cellular communications and a massive number of connected devices. 6G will enable a shift towards a continuous edge-to-cloud architecture. Current cloud solutions, where all the data is transferred and computed in the cloud, are not sustainable in such a large network of devices. Current technologies, including development methods, software architectures, and orchestration and offloading systems, still need to be prepared to cope with such requirements. In this paper, we conduct a Systematic Mapping Study to investigate the current research status of 6G Software Engineering. Results show that 18 research papers have been proposed in software process, software architecture, orchestration and offloading methods. Of these, software architecture and software-defined networks are respectively areas and topics that have received the most attention in 6G Software Engineering. In addition, the main types of results of these papers are methods, architectures, platforms, frameworks and algorithms. For the five tools/frameworks proposed, they are new and not currently studied by other researchers. The authors of these findings are mainly from China, India and Saudi Arabia. The results will enable researchers and practitioners to further research and extend for 6G Software Engineering.
Various ideation methods, such as morphological analysis and design-by-analogy, have been developed to aid creative problem-solving and innovation. Among them, the Theory of Inventive Problem Solving (TRIZ) stands out as one of the best-known methods. However, the complexity of TRIZ and its reliance on users' knowledge, experience, and reasoning capabilities limit its practicality. To address this, we introduce AutoTRIZ, an artificial ideation system that integrates Large Language Models (LLMs) to automate and enhance the TRIZ methodology. By leveraging LLMs' vast pre-trained knowledge and advanced reasoning capabilities, AutoTRIZ offers a novel, generative, and interpretable approach to engineering innovation. AutoTRIZ takes a problem statement from the user as its initial input, automatically conduct the TRIZ reasoning process and generates a structured solution report. We demonstrate and evaluate the effectiveness of AutoTRIZ through comparative experiments with textbook cases and a real-world application in the design of a Battery Thermal Management System (BTMS). Moreover, the proposed LLM-based framework holds the potential for extension to automate other knowledge-based ideation methods, such as SCAMPER, Design Heuristics, and Design-by-Analogy, paving the way for a new era of AI-driven innovation tools.
This paper introduces reAnalyst, a framework designed to facilitate the study of reverse engineering (RE) practices through the semi-automated annotation of RE activities across various RE tools. By integrating tool-agnostic data collection of screenshots, keystrokes, active processes, and other types of data during RE experiments with semi-automated data analysis and generation of annotations, reAnalyst aims to overcome the limitations of traditional RE studies that rely heavily on manual data collection and subjective analysis. The framework enables more efficient data analysis, which will in turn allow researchers to explore the effectiveness of protection techniques and strategies used by reverse engineers more comprehensively and efficiently. Experimental evaluations validate the framework's capability to identify RE activities from a diverse range of screenshots with varied complexities. Observations on past experiments with our framework as well as a survey among reverse engineers provide further evidence of the acceptability and practicality of our approach.
In the social and organizational sciences, accountability has been linked to the efficient operation of organizations. However, it has received limited attention in software engineering (SE) research, in spite of its central role in the most popular software development methods (e.g., Scrum). In this article, we explore the mechanisms of accountability in SE environments. We investigate the factors that foster software engineers' individual accountability within their teams through an interview study with 12 people. Our findings recognize two primary forms of accountability shaping software engineers individual senses of accountability: institutionalized and grassroots. While the former is directed by formal processes and mechanisms, like performance reviews, grassroots accountability arises organically within teams, driven by factors such as peers' expectations and intrinsic motivation. This organic form cultivates a shared sense of collective responsibility, emanating from shared team standards and individual engineers' inner commitment to their personal, professional values, and self-set standards. While institutionalized accountability relies on traditional "carrot and stick" approaches, such as financial incentives or denial of promotions, grassroots accountability operates on reciprocity with peers and intrinsic motivations, like maintaining one's reputation in the team.
Context: Experiment replications play a central role in the scientific method. Although software engineering experimentation has matured a great deal, the number of experiment replications is still relatively small. Software engineering experiments are composed of complex concepts, procedures and artefacts. Laboratory packages are a means of transfer-ring knowledge among researchers to facilitate experiment replications. Objective: This paper investigates the experiment replication process to find out what information is needed to successfully replicate an experiment. Our objective is to propose the content and structure of laboratory packages for software engineering experiments. Method: We evaluated seven replications of three different families of experiments. Each replication had a different experimenter who was, at the time, unfamiliar with the experi-ment. During the first iterations of the study, we identified experimental incidents and then proposed a laboratory package structure that addressed these incidents, including docu-ment usability improvements. We used the later iterations to validate and generalize the laboratory package structure for use in all software engineering experiments. We aimed to solve a specific problem, while at the same time looking at how to contribute to the body of knowledge on laboratory packages. Results: We generated a laboratory package for three different experiments. These packages eased the replication of the respective experiments. The evaluation that we conducted shows that the laboratory package proposal is acceptable and reduces the effort currently required to replicate experiments in software engineering. Conclusion: We think that the content and structure that we propose for laboratory pack-ages can be useful for other software engineering experiments.
Rebeca C. Motta, Káthia M. de Oliveira, Guilherme H. Travassos
Context: The Internet of Things (IoT) has brought expectations for software inclusion in everyday objects. However, it has challenges and requires multidisciplinary technical knowledge involving different areas that should be combined to enable IoT software systems engineering. Goal: To present an evidence-based roadmap for IoT development to support developers in specifying, designing, and implementing IoT systems. Method: An iterative approach based on experimental studies to acquire evidence to define the IoT Roadmap. Next, the Systems Engineering Body of Knowledge life cycle was used to organize the roadmap and set temporal dimensions for IoT software systems engineering. Results: The studies revealed seven IoT Facets influencing IoT development. The IoT Roadmap comprises 117 items organized into 29 categories representing different concerns for each Facet. In addition, an experimental study was conducted observing a real case of a healthcare IoT project, indicating the roadmap applicability. Conclusions: The IoT Roadmap can be a feasible instrument to assist IoT software systems engineering because it can (a) support researchers and practitioners in understanding and characterizing the IoT and (b) provide a checklist to identify the applicable recommendations for engineering IoT software systems.
This position paper for an invited talk on the "Future of eScience" discusses the Research Software Engineering Movement and where it might be in 2030. Because of the authors' experiences, it is aimed globally but with examples that focus on the United States and United Kingdom.
Gabriel C. Ullmann, Yann-Gaël Guéhéneuc, Fabio Petrillo
et al.
Game engines provide video game developers with a wide range of fundamental subsystems for creating games, such as 2D/3D graphics rendering, input device management, and audio playback. Developers often integrate these subsystems with other applications or extend them via plugins. To integrate or extend correctly, developers need a broad system architectural understanding. However, architectural information is not always readily available and is often overlooked in this kind of system. In this work, we propose an approach for game engine architecture recovery and explore the architecture of three popular open-source game engines (Cocos2d-x, Godot, and Urho3D). We perform manual subsystem detection and use Moose, a platform for software analysis, to generate architectural models. With these models, we answer the following questions: Which subsystems are present in game engines? Which subsystems are more often coupled with one another? Why are these subsystems coupled with each other? Results show that the platform independence, resource management, world editor, and core subsystems are frequently included by others and therefore act as foundations for the game engines. Furthermore, we show that, by applying our approach, game engine developers can understand whether subsystems are related and divide responsibilities. They can also assess whether relationships among subsystems are appropriate for the game engine.
Markus Borg, Elizabeth Bjarnason, Michael Unterkalmsteiner
et al.
The RET (Requirements Engineering and Testing) workshop series provides a meeting point for researchers and practitioners from the two separate fields of Requirements Engineering (RE) and Testing. The long term aim is to build a community and a body of knowledge within the intersection of RE and Testing, i.e., RET. The 4th workshop was co-located with the 25th International Requirements Engineering Conference (RE'17) in Lisbon, Portugal and attracted about 20 participants. In line with the previous workshop instances, RET 2017 o ered an interactive setting with a keynote, an invited talk, paper presentations, and a concluding hands-on exercise.
The strong interaction between an antikaon and a nucleon is at the origin of various interesting phenomena in kaon-nuclear systems. In particular, the interaction in the isospin $I=0$ channel is sufficiently attractive to generate a quasi-bound state, the $Λ(1405)$ resonance, below the $\bar{K}N$ threshold. Based on this picture, it may be expected that the $\bar{K}N$ interaction also generates quasi-bound states in kaon-nuclear systems, sometimes called kaonic nuclei. At the same time, the $\bar{K}N$ quasi-bound picture of the $Λ(1405)$ is also related to the discussion of hadronic molecules in hadron spectroscopy. Here an overview is presented of the theoretical studies developed for kaon-nucleon and kaon-nuclear systems. We start from the modern understanding of the $Λ(1405)$ resonance. We then discuss the $\bar{K}N$ interaction and various aspects of few-body kaonic nuclei. Heavier kaon-nuclear systems are examined from the viewpoint of nuclear many-body physics, with focus on the properties of antikaons in nuclear matter. Related topics, such as the $K^{-}p$ momentum correlation functions in high-energy collisions and the studies of kaonic atoms, are also discussed.
Cloud native information systems engineering enables scalable and resilient service infrastructures for all major online offerings. These are built following agile development practices. At the same time, a growing demand for privacy-friendly services is articulated by societal norms and policy through effective legislative frameworks. In this paper, we identify the conceptual dimensions of cloud native privacy engineering and propose an integrative approach to be addressed in practice to overcome the shortcomings of existing privacy enhancing technologies. Furthermore, we propose a reference software development lifecycle called DevPrivOps to enhance established agile development methods with respect to privacy. Altogether, we show that cloud native privacy engineering advances the state of the art of privacy by design and by default using latest technologies.
Strain provides an effective means to tune the electrical properties while retaining the native chemical composition of the material. Unlike three-dimensional solids, two-dimensional materials withstand higher levels of elastic strain making it easier to tune various electrical properties to suit the technology needs. In this work we explore the effect of uniaxial tensile-strain on the electrical transport properties of bi- and few-layered MoS2, a promising 2D semiconductor. Raman shifts corresponding to the in-plane vibrational modes show a redshift with strain indicating a softening of the in-plane phonon modes. Photo luminescence measurements reveal a redshift in the direct and the indirect emission peaks signalling a reduction in the material bandgap. Transport measurements show a substantial enhancement in the electrical conductivity with a high piezoresistive gauge factor of ~ 321 superior to that for Silicon for our bi-layered device. The simulations conducted over the experimental findings reveal a substantial reduction of the Schottky barrier height at the electrical contacts in addition to the resistance of MoS2. Our studies reveal that strain is an important and versatile ingredient to tune the electrical properties of 2D materials and also can be used to engineer high-efficiency electrical contacts for future device engineering.
Nowadays it is inevitable to use intelligent systems to improve the performance and optimization of different components of devices or factories. Furthermore, it's so essential to have appropriate predictions to make better decisions in businesses, medical studies, and engineering studies, etc. One of the newest and most widely used of these methods is a field called Data Science that all of the scientists, engineers, and factories need to learn and use in their careers. This article briefly introduced data science and reviewed its methods, especially it's usages in mechanical engineering and challenges and ways of developing data science in mechanical engineering. In the introduction, different definitions of data science and its background in technology reviewed. In the following, data science methodology which is the process that a data scientist needs to do in its works been discussed. Further, some researches in the mechanical engineering area that used data science methods in their studies, are reviewed. Eventually, it has been discussed according to the subjects that have been reviewed in the article, why it is necessary to use data science in mechanical engineering researches and projects.
An analogy is an identification of structural similarities and correspondences between two objects. Computational models of analogy making have been studied extensively in the field of cognitive science to better understand high-level human cognition. For instance, Melanie Mitchell and Douglas Hofstadter sought to better understand high-level perception by developing the Copycat algorithm for completing analogies between letter sequences. In this paper, we argue that analogy making should be seen as a core primitive in software engineering. We motivate this argument by showing how complex software engineering problems such as program understanding and source-code transformation learning can be reduced to an instance of the analogy-making problem. We demonstrate this idea using Sifter, a new analogy-making algorithm suitable for software engineering applications that adapts and extends ideas from Copycat. In particular, Sifter reduces analogy-making to searching for a sequence of update rule applications. Sifter uses a novel representation for mathematical structures capable of effectively representing the wide variety of information embedded in software. We conclude by listing major areas of future work for Sifter and analogy-making in software engineering.
According to different reports, many recent software engineering graduates often face difficulties when beginning their professional careers, due to misalignment of the skills learnt in their university education with what is needed in industry. To address that need, many studies have been conducted to align software engineering education with industry needs. To synthesize that body of knowledge, we present in this paper a systematic literature review (SLR) which summarizes the findings of 33 studies in this area. By doing a meta-analysis of all those studies and using data from 12 countries and over 4,000 data points, this study will enable educators and hiring managers to adapt their education / hiring efforts to best prepare the software engineering workforce.
Naomi E. Leonard, George F. Young, Kelsey Hochgraf
et al.
Flock Logic was developed as an art and engineering project to explore how the feedback laws used to model flocking translate when applied by dancers. The artistic goal was to create choreographic tools that leverage multi-agent system dynamics with designed feedback and interaction. The engineering goal was to provide insights and design principles for multi-agent systems, such as human crowds, animal groups and robotic networks, by examining what individual dancers do and what emerges at the group level. We describe our methods to create dance and investigate collective motion. We analyze video of an experiment in which dancers moved according to simple rules of cohesion and repulsion with their neighbors. Using the prescribed interaction protocol and tracked trajectories, we estimate the time-varying graph that defines who is responding to whom. We compute status of nodes in the graph and show the emergence of leaders. We discuss results and further directions.
A recent research trend has emerged to identify developers' emotions, by applying sentiment analysis to the content of communication traces left in collaborative development environments. Trying to overcome the limitations posed by using off-the-shelf sentiment analysis tools, researchers recently started to develop their own tools for the software engineering domain. In this paper, we report a benchmark study to assess the performance and reliability of three sentiment analysis tools specifically customized for software engineering. Furthermore, we offer a reflection on the open challenges, as they emerge from a qualitative analysis of misclassified texts.
This paper describes a novel approach to software engineering derived from the "SP Theory of Intelligence" and its realisation in the "SP Computer Model". Despite superficial appearances, it is shown that many of the key ideas in software engineering have counterparts in the structure and workings of the SP system. Potential benefits of this new approach to software engineering include: the automation or semi-automation of software development, with support for programming of the SP system where necessary; allowing programmers to concentrate on 'world-oriented' parallelism, without worries about parallelism to speed up processing; support for the long-term goal of programming the SP system via written or spoken natural language; reducing or eliminating the distinction between 'design' and 'implementation'; reducing or eliminating operations like compiling or interpretation; reducing or eliminating the need for verification of software; reducing the need for validation of software; no formal distinction between program and database; the potential for substantial reductions in the number of types of data file and the number of computer languages; benefits for version control; and reducing technical debt.
Studies related to human aspects in software engineering (SE) have been performed from different perspectives. These perspectives include the study of human factors in different phases of software life cycle, effect of team performance in software development, how can a personality trait suit a particular task, and about some other miscellaneous issues. This research work aims to establish personality profiles of Pakistani software engineers using the Myers-Briggs Type Indicator (MBTI) instrument. In this survey, we have collected personality profiles of 110 software engineers. Moreover, their preferences of software quality attributes have also been collected. Analysis of the study shows that the most prominent personality type is a combination of introversion, sensing, thinking and judging. Investigative results indicate that most of the software engineers consider usability and functionality as the most important software quality attributes.