One effective programming education approach is to involve competitive factors in project-based learning (PBL). For competitive PBL to be effective, students must be self-motivated, and teachers must monitor students' progress in development. This paper proposes a competitive PBL support framework called continuous competition (CC) based on the concept of continuous integration (CI), a software development practice. In traditional CI, program modification (i.e., commit) triggers automatic build and test to achieve small and rapid development iterations. Our propsosed CC conducts a competition of students' products in addition to build and test when students change their program. As a case study, we introduced CC into an actual competitive PBL course held at Osaka University. We observed a certain contribution to improving students' motivation in development and helping teachers monitor students' progress.

Project-based learning (PBL) has been widely introduced in higher education as a practical, efficient and student-centered learning method. One of the important factors to achieve effective PBL is student-centered process management. However, these task management skills are difficult to conduct for students because it has no obvious answer and requires experience rather than knowledge. The goal of this study is to promote acquisition task management skills by students themselves at PBL, and we develop a work breakdown structure (WBS) tool with aiming task definition, organization and assignment. The tool evaluation is conducted on our university's PBL. According to the evaluation, students who use the tool were more aware of their task management than students who do not use it.

This study investigates the relationship between prior learning, motivation, and learning outcomes in cybersecurity education from the perspective of gamification within the context of the KOSEN Security Educational Community (K-SEC) project and the Highly Advanced Cybersecurity for KOSEN (HACK) project. A series of cybersecurity exercises were conducted with 40 KOSEN students over two semesters, and their self-assessed knowledge, skills, and motivation were measured. The results demonstrate that prior learning significantly contributed to an increase in students' motivation and knowledge, and students with increased/unchanged motivation exhibited a positive correlation between motivation and learning outcomes in subsequent exercises. Our findings provide empirical evidence for the effectiveness of prior learning in enhancing students' motivation and the positive influence of increased/unchanged motivation on learning outcomes in cybersecurity education. We contribute to the growing body of knowledge on the application of gamification in cybersecurity education and offer insights into the design and implementation of effective cybersecurity education programs within the unique context of KOSEN's five-year engineering education model.

Insufficient requirements acquisition due to insufficient capabilities of system engineers (SEs) is a serious problem in software development. Insufficient requirements acquisition from customers causes rework in downstream processes and delays in schedules. On the other hand, the emergence of generative AI, which can learn a wealth of knowledge from the Web and interact with customers based on that knowledge, may solve this problem. In this study, ChatGPT was used as an SE and the experimenter as a customer, and prompts were given to ChatGPT for requirements acquisition. The experimental results showed that ChatGPT was able to acquire about 42.2% of the total requirements. We also confirmed that ChatGPT's ability to acquire requirements decreases as the questions become longer, the number of non-functional requrement questions such as security increases, and ChatGPT intensively asks questions related to the keywords given in the initial requrements.

The purpose of this study is to increase society's awareness of email security and to reduce the damage due to targeted emails by developing an "automatic delivery system using training targeted emails generated from self-made targeted emails". This system will enable companies to proactively conduct targeted email training. In our previous study, we developed a system that automatically creates training targeted emails generated from self-made targeted emails using Markov chains and multiple Markov chains. However, targeted email training was not able to be conducted using the developed automatic targeted-email generation system because a system had not yet been developed for delivering automatically generated training targeted emails. In this study, we developed a system that delivers automatically generated training targeted emails and enables targeted email training using training targeted emails generated from self-made targeted emails.

Generating preferred images using generative adversarial networks (GANs) is challenging owing to the high-dimensional nature of latent space. In this study, we propose a novel approach that uses simple user-swipe interactions to generate preferred images for users. To effectively explore the latent space with only swipe interactions, we apply principal component analysis to the latent space of the StyleGAN, creating subspaces composed of principal components that significantly change the appearance of the image. We use a multi-armed bandit algorithm to decide the dimensions to explore, focusing on the preferences of the user. Experiments show that our method is more efficient in generating preferred images than the baseline methods. Furthermore, changes in preferred images during image generation or the display of entirely different image styles were observed to provide new inspirations, subsequently altering user preferences.

When adding another service to an existing service, focusing on the stakeholders who have a need for the additional service may omit consideration of the impact on other stakeholders when eliciting requirements. On the other hand, stakeholders who do not have a direct need for additional services may be less interested in adding services, making it difficult to elicit their needs through interviews. In addition, service additions may cause problems due to the interaction between services, but it is generally difficult to anticipate such problems, and it is difficult to elicit problems and countermeasures through interviews. Therefore, we devised a method to identify the needs of stakeholders using the CATWOE analysis, a requirements analysis method, and to elicit requirements using the STAMP/STPA safety analysis method, which focuses on interactions by considering failure to satisfy needs as a loss. A case study is presented in which the devised method is applied to a past accident case, and the results are shown to confirm its effectiveness.

Fuzzing is a technique for detecting vulnerabilities through rapid test case generation and execution. AFL, a well-known fuzzing tool, efficiently explores test cases that pass through previously undiscovered paths by observing execution paths at the basic block level. In this study, we investigate the change in AFL path search efficiency by aggregating code clones of the target source code. We hypothesize that aggregating code clones that contain basic blocks would aggregate paths that AFL can detect, reducing the number of paths observed in AFL and making it easier to reach undiscovered paths. The experimental results showed no statistically significant difference in the number of paths discovered by AFL, but code clone aggregation did change the test cases generated by AFL, detecting one undiscovered crash.