Software Engineering Education Research Articles

Development of Arduino applications for IoT applications in software engineering education: a systematic literature review

The continuous development of software applications is a necessary step in producing high-quality products that will consistently meet end-user expectations and stakeholder needs. Development of Arduino applications, embedded in a product’s hardware, can and should be considered at the software engineering phase itself, even though current practice dictates it be handled by product engineers. The method used in this investigation was based on a systematic literature review (SLR). Therefore, this paper depicts a gap that currently exists within the body of literature surrounding the development of Arduino applications for ‘internet of things’ (IoT) applications in software engineering education in commercial and research fields. The result of this study are two findings investigates: i) relevant Arduino application development used in software engineering and ii) method for applying software engineering for Arduino applications. The limitations and constraints of each technique in respect to Arduino apps were also examined in order to provide a better understanding of each body of study's weaknesses and strengths. We realise that these studies are still insufficient and need to be evaluated and improved further.

Software Engineering in Nigeria: Trends and Challenges

Bringing theory and practice together is aspiration of software engineering education, so that the learner may acquire a deep understanding on basic concepts and principles, along with skills and competences to solve real-world problems. Thus, it is not just an academic concern about teaching relevant topics, but also a task to shape skilled individuals equipped for the industry demand. Moreover, due to the rapid development the software industry is and transnational nature, several challenges are challenging the qualification of software engineers, capable to develop products according to the international standards into markets overseas. Agile software development methods are the generally used and prominent software development methods. Agile methods are simple, easy and deliver software faster than the traditional software development methods. Software development in Nigeria is at developing stage.

Construction and Exploration of Experimental Teaching Content in Software Engineering from a Systems Theory Perspective

Software engineering, as a branch of traditional computer science, distinguishes itself from other computer-related disciplines by its emphasis on practical application. Experimental teaching in software engineering, integral to professional education, serves as a crucial means to enhance students’ practical skills. The content of experimental teaching, serving as a vessel for such instruction, plays a pivotal role in the holistic development of students. Addressing the current fragmented state of experimental teaching in applied universities, this study introduces a holistic and systematic view based on systems theory. By integrating the fundamental principles and methods of systems theory with the distinctive characteristics of software engineering, this study focuses on analyzing the holistic technology of software implementation and the interrelationships among various technical elements of software systems within the field of software engineering. From a holistic-part-holistic analytical perspective, this research constructs an analytical model for experimental teaching content in software engineering under the lens of systems theory. The study elaborates on the construction method of this model and its specific implementation details from the perspectives of talent cultivation programs, engineering elements, and concentrated practical exercises. Practical evidence indicates that the implementation of this experimental teaching content effectively enhances students’ practical skills in software engineering, elevates students’ recognition of their field of study, boosts their confidence in job placement, and provides robust support for seamless transition from graduation to employment. Furthermore, this study sheds light on the construction of experimental teaching content in computer-related fields in engineering disciplines at other applied universities.

GPTSniffer: A CodeBERT-based classifier to detect source code written by ChatGPT

Since its launch in November 2022, ChatGPT has gained popularity among users, especially programmers who use it to solve development issues. However, while offering a practical solution to programming problems, ChatGPT should be used primarily as a supporting tool (e.g., in software education) rather than as a replacement for humans. Thus, detecting automatically generated source code by ChatGPT is necessary, and tools for identifying AI-generated content need to be adapted to work effectively with code. This paper presents GPTSniffer– a novel approach to the detection of source code written by AI – built on top of CodeBERT. We conducted an empirical study to investigate the feasibility of automated identification of AI-generated code, and the factors that influence this ability. The results show that GPTSniffer can accurately classify whether code is human-written or AI-generated, outperforming two baselines, GPTZero and OpenAI Text Classifier. Also, the study shows how similar training data or a classification context with paired snippets helps boost the prediction. We conclude that GPTSniffer can be leveraged in different contexts, e.g., in software engineering education, where teachers use the tool to detect cheating and plagiarism, or in development, where AI-generated code may require peculiar quality assurance activities.

Taxonomy of inline code comment smells

Code comments play a vital role in source code comprehension and software maintainability. It is common for developers to write comments to explain a code snippet, and commenting code is generally considered a good practice in software engineering. However, low-quality comments can have a detrimental effect on software quality or be ineffective for code understanding. This study aims to create a taxonomy of inline code comment smells and determine how frequently each smell type occurs in software projects. We conducted a multivocal literature review to define the initial taxonomy of inline comment smells. Afterward, we manually labeled 2447 inline comments from eight open-source projects where half of them were Java, and another half were Python projects. We created a taxonomy of 11 inline code comment smell types and found out that the smells exist in both Java and Python projects with varying degrees. Moreover, we conducted an online survey with 41 software practitioners to learn their opinions on these smells and their impact on code comprehension and software maintainability. The survey respondents generally agreed with the taxonomy; however, they reported that some smell types might have a positive effect on code comprehension in certain scenarios. We also opened pull requests and issues fixing the comment smells in the sampled projects, where we got a 27% acceptance rate. We share our manually labeled dataset online and provide implications for software engineering practitioners, researchers, and educators.

Integrating Usability into Software Engineering Course Projects

This study presents a case study of the integration of usability evaluation into the development of task based information system prototypes in undergraduate software engineering course projects within a problem-based learning (PBL) approach. As part of the course, usability evaluation was integrated into the software development process, and the usability evaluation performance (UEP) of the projects was assessed in terms of the following criteria: effectiveness, efficiency and problem validity. It also analyzed the problem solution rate of the projects and their correlation between UEP criteria. Additionally, the study in-depth analyzed the students' final conclusions regarding the design process. The relationship between UEP criteria and the problem solving rate was examined using Spearman correlation analysis. In addition, final conclusions and problem solving behaviors were analyzed through document and thematic analysis techniques. The results supported that there is a significant correlation between usability evaluation criteria and problem solving rate. The main themes that emerged regarding usability problem solving behaviors were navigation design, error handling, database connection, algorithm design, search matching, system-user communication design and others. Final conclusions showed that project teams gained valuable insights into user-centered design, solved critical usability problems and improved their prototype design. It can be concluded that the integration of usability into software engineering education using the above approach contributes to students' understanding of user-centered interaction design.

E-learning in Software Engineering Education for Computer Software Development in the Big Data Context

E-learning in Software Engineering Education for Computer Software Development in the Big Data Context

C4 Skills in the Engineering Graduate: A Study to Align Software Engineering Education With Market-Driven Software Industry Needs

<i>C4</i> Skills in the Engineering Graduate: A Study to Align Software Engineering Education With Market-Driven Software Industry Needs

Delivering computing module for the large part-time software development class from pre- to post-pandemic: An online learning experience

IntroductionCovid-19 pandemic brought dramatic changes to higher education settings, particularly for curriculum delivery, moving quickly to online learning. This paper discusses teaching experience covering practices of technology-enhanced learning for the MSc Computing Foundations module (20 CATS) for a large class studying part-time Software Engineering course at the School of Electronics, Electrical Engineering and Computer Science (EEECS), Queen's University Belfast (QUB) during 2019–2022 academic years. We compare on-campus learning with the abrupt shift to online learning during the pandemic and with sustainable online learning a year later. The objective of this study is to answer how part-time software development students perceived their technology-enhanced learning experience from pre- to post-pandemic and to evaluate the impact of the shift to online learning for the part-time class MethodsThis study is based on data collected during 2019/2020, 2020/2021, 2021/2022 academic years. Methodology types employed in this study include online observation with statistics collected from the virtual learning environment (VLE) Canvas, quantitative analysis, individual student surveys on teaching techniques and module content ResultsThis study provided an effective online teaching method for Computer Foundation module, reviewing the impact of different curriculum items and online educational activities starting with content delivery – both synchronous and asynchronous – and moving on to VLE Canvas discussion forums, ungraded formative quizzes, in-term formative assessment in the form of mock exam and, finally, to online summative assessment delivered on VLE Canvas. We investigate positive and negative aspects of technology-enhanced learning from pre- to post-pandemic according to part-time adult students studying the MSc software development program and focus on how this effective learning environment contributes to education practice with a view what developments are worth retaining post-pandemic and what did not work well. Analysis of the data from individual student surveys on teaching techniques and module content for the Computing Foundations module allowed us to conclude that students perceived very positive their technology-enhanced learning experience after shifting to online learning. We also found out that сhanging the module delivery format (from face-to-face to online) did not affect the results of the students’ performance. Conclusion and implicationsAdaptation of the MSc Computing Foundation module to a new model of distance learning has proved to be successful, so we can conclude that this delivery format is appropriate for this target audience. This study explored the effectiveness of the pedagogical approach while also gaining valuable insights into the software development student experience of learning in the VLE. The findings from this study may contribute to developing effective teaching practices in software engineering education and adult learning, and improve the preparation of future software professionals in the IT industry.

Research on the Application of Project Teaching Method in the New Model of Software Engineering Course

Software engineering course is one of the core courses of computer science. The students trained in the current teaching mode can no longer meet the market demand for high-technology talents. Based on this, the research attempts to optimize the traditional software engineering teaching mode by using the project teaching method (PTM). According to the basic concept of PTM and course characteristics, the reform path of PTM in software engineering course is explored in the experiment. Then in the experiment, the indicators that affect teaching reform effect is selected and a evaluation model is built. And GA-BP algorithm is used to evaluate the effect of the evaluation model. To verify the performance of the built model and the final evaluation effect, the research results are tested from the fitness of the algorithm, error performance, prediction in the data set and other aspects. GA-BP algorithm converged when it iterated to the 18th generation, and the final fitness value was 0.61. The average error square value of GA-BP was 0.35 and the minimum error square sum of GA-BP was 0.48. Its prediction accuracy in test set and training set was 93.4% and 94.1% respectively. The maximum prediction error in the training sample was only 0.015, and the performance of the above data was better than the other three algorithms. To sum up, applying PTM to software engineering curriculum reform can achieve better teaching results.

Thriving in the era of hybrid work: Raising cybersecurity awareness using serious games in industry trainings

The important missions of modern software engineering education are to prepare software engineers to work in a hybrid mode and to address the need to enable them to write secure code and deliver secure products and services to the customer. Providing training akin to an authentic experience poses several challenges, such as hybrid infrastructures, lack of engagement, and interactions. Cybersecurity and cybersecurity awareness have also gained importance due to the shift towards work-from-home (WFH) or work-from-anywhere (WFA): The work environment is forced to be distributed across large heterogeneous networks with different security levels. We perceive hybrid work as a work mode where the team members follow WFH or WFA and work from the office. Therefore various security levels at the workplace and restrictions on informal team communications need to be taken into account.We report on experiences from an industrial company producing software and cyber–physical systems. Initially set to update the existing secure code guidelines, the study lead to the discovery that it is crucial to go beyond an up-to-date set of security guidelines: it is mandatory to raise the cybersecurity awareness of those who are to follow the guidelines. We present a novel approach, via serious games, to train software engineers working in the industry, which is delivered in a hybrid mode and equips practitioners to face the challenges of hybrid work. Serious games have more than just entertainment purposes. They have proven effective ways to maintain engagement and boost training, particularly in cybersecurity. We developed and used two innovative serious games to raise cybersecurity awareness: (1) CyberSecurity Challenges (CSC), about how to develop secure software; and (2) Cloud of Assets and Threats (CATS), about cloud security, including its shared responsibility model. It is decisive for the industry that the software is written, developed, and deployed securely. The cloud service has replaced many on-premises deployments. It is essential to enable hybrid work, turning knowledge and practice about cloud security into essential capacities for professional hybrid work.We provide the theoretical foundations for the two serious games and the overall approach. We also report and analyze more than 300 industry practitioners’ training experiences from 2017 to 2023 and use this to evaluate the games. By applying serious games in the industry, among practitioners, we gain valuable experience in combining the advantage of different training modes and mitigating the disadvantage of online training. We observe the impact of serious games through a scientifically-sound approach based on the data and feedback we collected systematically from the trainers’, trainees’, and organization’s perspectives. We show through empirical evidence that serious games are a successful approach for training conducted in hybrid work mode while providing authentic and immersed experiences that empower and raise cybersecurity awareness of current and future software professionals.

Comparing a LEGO® Serious Play Activity with a Traditional Lecture in Software Engineering Education

Comparing a LEGO® Serious Play Activity with a Traditional Lecture in Software Engineering Education

Practical Application of AI and Large Language Models in Software Engineering Education

Practical Application of AI and Large Language Models in Software Engineering Education

Transcompiler CNN-GMN Based Cross Language Clone Detection and Algorithm Recognition

Abstract: Software replication stemming from code reutilization introduces complexities in software maintenance. While deep learning-driven clone detection tools abound, they predominantly cater to singular programming languages. Concurrently, the task of identifying algorithms within program code remains challenging due to the absence of metadata, rendering the selection process intricate. This research seeks to establish synergies between these disparate domains by advancing innovative methodologies. The study delves into the unexplored domain of harnessing data augmentation, particularly leveraging transcompiler-based techniques, to enhance the discernment of code clones across different programming languages. Drawing upon the insights derived from transcompilers, this investigation applies data augmentation through source-to-source translation, exemplified by the Transcoder. This method extends the applicability of single-language models, such as the Graph Matching Network (GMN), to encompass cross-language detection. Concurrently, a program code classification model is introduced, leveraging Convolutional Neural Networks (CNNs) to discern algorithms based on structural features (SFs). These SFs are extracted from program codes and subsequently transformed into a one-hot binary matrix. The CNN model undergoes meticulous fine-tuning, optimizing its structural configurations and hyperparameters for superior algorithm classification. This research signifies an integration of disparate realms, intertwining cross-language clone detection and algorithm identification within a unified framework. The exploration of data augmentation for cross-language clone detection and the employment of CNN-GMNs for algorithm identification converge to furnish significant contributions to both domains. These insights, thus, hold promise for advancing the fields of software engineering and programming education

Teaching Reform Measures For Software Engineering Specialization In The Context Of New Engineering

With the rapid development of science and technology and the deepening of industrial change, the new engineering discipline has become an important direction of higher education reform. As an important part of the new engineering, the reform of software engineering education mode is of great significance for the cultivation of software engineers who can adapt to the future development. This paper firstly introduces the characteristics of software engineering under the background of new engineering discipline, then deeply analyzes the problems existing in the current teaching of software engineering, and puts forward the targeted reform measures. By practicing these reform measures, we can effectively improve the teaching quality of software engineering and cultivate more excellent software engineers with innovative ability and practical ability.

An Observational Study on React Native (RN) Questions on Stack Overflow (SO)

Mobile applications are continuously increasing in prevalence. One of the main challenges in mobile application development is creating cross-platform applications. To facilitate developing cross-platform applications, the software engineering community created several solutions, one of which is React Native (RN), which is a popular cross-platform framework. The software engineering literature demonstrated the effectiveness of Stack Overflow (SO) in providing real-world perspectives on a variety of technical subjects. Therefore, this study aims to gain a better understanding of the stance of RN on SO. We identified and analyzed 131,620 SO RN-related questions. Moreover, we observed how the interest toward RN on SO evolves over time. Additionally, we utilized Latent Dirichlet Allocation (LDA) to identify RN-related topics that are discussed within the questions. Afterward, we utilized a number of proxy measures to estimate the popularity and difficulty of these topics. The results revealed that interest toward RN on SO was generally increasing. Moreover, RN-related questions revolve around six topics, with the topics of layout and navigation being the most popular and the topic of iOS issues being the most difficult. Software engineering researchers, practitioners, educators, and RN contributors may find the results of this study beneficial in guiding their future RN efforts.

Investigating Students’ Acceptance of Moodle E-Learning Platform for Teaching and Learning during COVID-19 Pandemic at Makerere University Business School

The aim of this study was to investigate online teaching and learning using Moodle for teaching Software Engineering in the second-year Business Computing undergraduate class at Makerere University Business School (MUBS). The study was done during the COVID-19 pandemic when most lectures were conducted online with few face-to-face sessions. The Technology Acceptance Model (TAM) and the theory of reasoned action (TRA) theoretical frameworks were used to direct the research. A questionnaire was designed and administered to a group of 145 students selected as a sample from a class 180 students. The data collected was analysed to determine correlations between latent variables and variances of dependable variables explained by independent variables. The correlation between the intention to use and the actual use of the system was high (r = 0.555). This implies that students’ acceptance of online teaching and learning was high. The results indicate that system accessibility and self-efficacy explain 27% of the variance in the perceived ease of use (adjusted R square = 0.27). The regression model was significant (sig. &lt; 0.05). The results also indicate that perceived usefulness and perceived ease of use explain 41.9% of the variance in intention to use (adjusted R square = 0.419). This regression model was significant (sig. &lt; 0.05). MUBS successfully implemented e-learning platform (MUBSEP) for emergency teaching during the COVID-19 pandemic lockdown. The teaching staff and the students used online educational tools for live classes and end-of-semester assessments. The use blended learning approach post COVID-19 pandemic is recommended for higher education.

Authorship Verification in software engineering education

The prevalence of academic misconduct, specifically contract cheating, is a rising concern in higher education institutions globally. Among the recent advancements, Generative Artificial Intelligence (genAI) has exacerbated the situation by offering authentically generated writings, making detection through traditional plagiarism tools ineffective. This paper explores the development and application of students' academic writing profiles, using a combination of word embedding (Word2Vec) and stylistic feature extraction techniques. By leveraging a Siamese neural network, our method focuses on recognising distinctive writing styles, a concept rooted in Authorship Verification (AV). Our approach's efficacy evaluates favourably against other AV methods and is tested against AI-generated texts deliberately designed to mimic student writing. The study emphasises the importance of understanding individual academic writing styles to identify outsourcing or AI-generated work effectively.

Software Engineering Education and Training: Industry Demands, Curriculum Deficits, and Pedagogy Direction

Software Engineering Education and Training: Industry Demands, Curriculum Deficits, and Pedagogy Direction

Software Engineering Education for Technical Engineering Degrees: A Comparison With the Needs of Robotics Software Engineering Education

Software Engineering Education for Technical Engineering Degrees: A Comparison With the Needs of Robotics Software Engineering Education