Empathy models and software engineering — A preliminary analysis and taxonomy

An Internet-based computing platform for the boundary element method

As that of coordination and ISO 15288, discipline system engineering and software engineering processes, methods have a lot in common, and concerns. In most of the system software interest, now offers an important function, more and more is the most significant factors determine the non-functional attributes. In the implementation of the system collection and failure are often attributed to the system and/or software engineering recovery. Need to use systems engineering software design and development, and need to incorporate software engineering concepts into system engineering, suggests that the decision of the two disciplines the close relationship between is appropriate. This article describes a definite theory integrating the two disciplines, defines a framework on establishing a vocabulary integration, and gives the use of this framework preliminary results to describe the system and software integration of defense plans.

Software engineering is a socio-technical endeavor, and while many of our contributions focus on technical aspects, human stakeholders such as software developers are directly affected by and can benefit from our research and tool innovations. In this paper, we question how much of our research addresses human and social issues, and explore how much we study human and social aspects in our research designs. To answer these questions, we developed a socio-technical research framework to capture the main beneficiary of a research study (the who), the main type of research contribution produced (the what), and the research strategies used in the study (how we methodologically approach delivering relevant results given the who and what of our studies). We used this Who-What-How framework to analyze 151 papers from two well-cited publishing venues---the main technical track at the International Conference on Software Engineering, and the Empirical Software Engineering Journal by Springer---to assess how much this published research explicitly considers human aspects. We find that although a majority of these papers claim the contained research should benefit human stakeholders, most focus on technical contributions without engaging humans in their studies. Although our analysis is scoped to two venues, our results suggest a need for more diversification and triangulation of research strategies. In particular, there is a need for strategies that aim at a deeper understanding of human and social aspects of software development practice to balance the design and evaluation of technical innovations. We recommend that the framework should be used in the design of future studies in order to nudge software engineering research towards explicitly including human and social concerns in their designs, and to improve the relevance of our research for human stakeholders.

This paper describes an approach to integrating software engineering concepts and principles into the Electrical and Computer Engineering (ECE) and Computer Science (CS) curricula. Our philosophy is to apply software engineering techniques throughout the ECE/CS curricula to leverage learning in non-software engineering courses. Our technique is to seek out faculty interested in innovative teaching techniques, consult with them to identify some way that they and we feel a course they are teaching could benefit pedagogically from some application of software engineering, and work with them to make that happen. The chief intended result is to leverage learning in diverse courses, thereby benefiting pedagogy of non-software engineering topics. An auxiliary important result is to increase awareness among both students and faculty of the software engineering body of knowledge. Many software engineering approaches to understanding and solving problems in the software life cycle can also address a variety of learning needs across disciplines in ECE and CS. For example, there are software engineering techniques that can emphasize visualization (benefiting students who respond to the visual modality), logical sequences (benefiting sequential learners), summarizations (benefiting global learners), and others. Additionally, general issues of teamwork and the engineering life cycle can be addressed. We have applied our approach to a diverse set of electrical engineering and computing courses at four universities in the US and Canada, and based on those experiences we believe we have identified a win-win paradigm that can be a model for integration of software engineering concepts into electrical engineering and computing curricula.

This article reviews the integration of machine learning (ML) techniques into Software Engineering (SE) across various phases of the software development life cycle (SDLC). The purpose is to investigate the applications of ML in SE, analyze its methodologies, present findings, and draw conclusions regarding its impact. The study categorized ML applications in SE and assessed the performance of various ML algorithms. Authors identified ML applications in SDLC phases, including requirements analysis, design, implementation, testing, and maintenance. ML algorithms, such as supervised and unsupervised learning, are employed for tasks like software requirement identification, design pattern recognition, code generation, and automated testing. In summary, we find that ML-based techniques are experiencing a substantial surge in adoption within the field of software engineering. Nevertheless, it is evident that substantial endeavors are needed to establish thorough comparisons and synergies among these approaches, perform meaningful evaluations grounded in detailed real-world implementations that are applicable to industrial software development. Therefore, our key takeaway is the necessity for a shift in focus towards reproducible research, prioritizing this over isolated novel concepts. Failure to do so may result in the limited practical implementation of these promising applications.

This paper introduces a method for incorporating software engineering concepts into the computer science curriculum earlier by using a structured project in CS1. The project is designed such that it must be completed using phases of a software development cycle. This approach provides the students earlier exposure to software engineering and grounds software engineering practice throughout the curriculum. With a better understanding of and appreciation for the software development process, students are better prepared to handle future academic and professional projects. This paper presents a detailed discussion of the CS1 project and its execution, accompanied by results and feedback from a post-project survey administered to students.

One application of software engineering is the vast and widely popular video game entertainment industry. Success of a video game product depends on how well the player base receives it. Of research towards understanding factors of success behind releasing a video game, we are interested in studying a factor known as Replayability. Towards a software engineering oriented game design methodology, we collect player opinions on Replayability via surveys and provide methods to analyze the data. We believe these results can help game designers to more successfully produce entertaining games with longer lasting appeal by utilizing our software engineering techniques.

Artificial Intelligence (AI) is becoming a fundamental component of software engineering, both in terms of using AI applications in software engineering (AI for SE) and using software engineering to develop AI systems (SE for AI).The SWEBOK highlights relevant AI knowledge areas, but in practice, AI impacts all aspects of software engineering.As a result, software engineering education needs to take account not only of a set of knowledge areas but also a foundational set of AI literacies that inform students about the wider and deeper implications of AI systems.This article addresses this challenge by mapping knowledge areas from the SWEBOK to the Scaffolded AI Literacy (SAIL) Framework.Using some example mappings from knowledge areas to AI literacies, followed by illustrative examples of teaching scenarios, it explores how SE knowledge areas can be supported and enhanced by developing AI literacies.

Transformers are the current state-of-the-art of natural language processing in many domains and are using traction within software engineering research as well. Such models are pre-trained on large amounts of data, usually from the general domain. However, we only have a limited understanding regarding the validity of transformers within the software engineering domain, i.e., how good such models are at understanding words and sentences within a software engineering context and how this improves the state-of-the-art. Within this article, we shed light on this complex, but crucial issue. We compare BERT transformer models trained with software engineering data with transformers based on general domain data in multiple dimensions: their vocabulary, their ability to understand which words are missing, and their performance in classification tasks. Our results show that for tasks that require understanding of the software engineering context, pre-training with software engineering data is valuable, while general domain models are sufficient for general language understanding, also within the software engineering domain.

As a basis subject of computer, Computer software engineering has been increasingly used in various fields, Games management system has been pre-designed on the basis of application of software engineering theory and method. The actual games management system software has been developed from using software engineering methods, in the actual design and development, it has been proved that a practical system has been perfect created with efficiency design, fully functional form, rational and affordable design.

Granulation is an important component of granular computing (GrC) as a problem solving paradigm. Specification and regulation of granulation are necessary in helping researchers and practitioners apply GrC into different applications. At present, there is insufficient investigation of this topic. This paper defines concepts and mechanisms of problem solving, investigates the fundamental principles and processes of granulation and demonstrates that the development of software engineering methodologies follows the basic idea of GrC: granulation. With the guide of granulation, this paper also proposes a new method of granulation by rolification (the act or the result of designing roles), i.e., roles and agents. The major contribution of this paper is establishing the foundations of granular problem solving and clarifying the relationships between granular problem solving and software engineering.

The Wright brothers' first flight took place in 1903, and for the next 60 years flight was purely a mechanical activity. This all changed in the early 1960s, when computers first began to find their way onto airplanes. The first onboard computers were hybrid electromechanical devices and made use of very specialized and limited instruction sets. There was a substantial lag between the appearance of digital technology in military aircraft and those flown in civilian applications. Although many people view aviation as an extremely high technology field, there is often a lag in technology adoption, particularly in the airborne segment of civil aviation. This lag is the result of conservatism due to safety concerns as well as a need to constrain both the weight and the cost of onboard systems. While slow to embrace the digital age, aviation‐related applications of software engineering are now at the forefront of this new science. Software applications are now found in all phased of aviation and include among the many, aircraft design, flight safety, air traffic control, maintenance, etc. Ever greater levels of automation onboard aircraft and in ATC systems require highly complex designs while demanding the utmost reliability and dependability of the software and the hosting hardware. Time and experience has shown that such reliability is the result of disciplined, well‐planned, thorough development processes. Current regulatory guidance for airborne systems containing software invokes DO‐178B/ED‐12B as the design assurance standard of choice to ensure safe software. Where software blends with hardware to produce firmware in the form of application‐specific integrated circuits and programmable logic devices, a new standard, DO‐254/ED‐80, has been introduced to extend design assurance to this area of software engineering for airborne systems. The software engineering approaches and techniques discussed here are a result of ongoing debate and consensus among the industry, regulators, and researchers. There is much engineering judgment in the techniques described, although there is no empirical basis or measurement of how best these techniques can eliminate safety‐related problems. Models have been developed to predict the latent fault density by correlating the maturity of the development process. Such models have not yet been applied and tested in the aviation industry. It should be noted that as the amount of software has steadily risen in commercial aircraft, the fatal accident rate has steadily fallen over the same time period. While a direct cause–effect relationship has yet to be established between the design assurance approaches discussed in this article and overall system safety, the trends provide significant confidence in our modern aerospace infrastructure.

This paper introduces the Knowledge View concept and its application in software engineering. The work presented is aimed at bringing knowledge engineering and Semantic Web technologies closer to software engineers and programmers. It is done by disguising knowledge bases as relational or object databases and applying patterns known to software engineers.

Depicting applications of several machine learning approaches in software systems development and deployment, this text provides analysis, characterization, and refinement of software engineering data in terms of machine learning methods.

A metamodel-driven approach to collaborative software engineering is presented. The approach employs optimistic replication of UML models and a novel semantics-based method for reconciliation of long-lived transactions in collaborative environments using metamodel information. The research described addresses the general problem of semantically consistent and functionally meaningful reconciliation of divergent replicas and enables significant simplification and formalization of its solution. The software engineering application is presented to illustrate how the approach can be effectively applied for cooperatively elaborated UML diagrams under strong consistency guarantees for produced software artifacts.