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1. Teaching Testing with Modern Technology Stacks in Undergraduate Software Engineering Courses

   https://doi.org/10.1145/3430665.3456352  

   Chow, Scott P.; Komarlu, Tanay; Conrad, Phillip T. (June 2021, ITICSE 2021)

   null (Ed.)

   Students’ experience with software testing in undergraduate computing courses is often relatively shallow, as compared to the importance of the topic. This experience report describes introducing industrial-strength testing into CMPSC 156, an upper division course in software engineering at UC Santa Barbara . We describe our efforts to modify our software engineering course to introduce rigorous test-coverage requirements into full-stack web development projects, requirements similar to those the authors had experienced in a professional software development setting. We present student feedback on the course and coverage metrics for the projects. We reflect on what about these changes worked (or didn’t), and provide suggestions for other instructors that would like to give their students a deeper experience with software testing in their software engineering courses. 

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2. A Secure Software Engineering Design Framework for Educational Purpose

   https://doi.org/10.1109/eIT53891.2022.9837112  

   Angulo, Abel A.; Yang, Xiaoli; Niyaz, Quamar; Paheding, Sidike; Javaid, Ahmad Y (May 2022, 2022 IEEE International Conference on Electro Information Technology (eIT))

   Ensuring software security is a critical task for a deliverable software system in today’s world, and its proper implementation guarantees the quality and security of the information ingested, stored, and processed by the system. It is imperative to introduce computer science and computer engineering students (CS/CE) with the secure software design practices early in their curriculum. This approach will help them understand fundamentals of secure programming, vulnerabilities in software systems, and secure software development before joining the industry workforce. In this paper, we propose an educational framework that integrates software security concepts in a software engineering design course. We envision that the framework will engage CS/CE students applying security principles and practices in different phases of the software development life cycle (SDLC) process. Our work focuses on review of common security requirements, policies, and mechanisms related to specific use cases as well as how those requirements are defined during the software design. 

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3. Improving Student Learning in Hardware Security: Project Vision, Overview, and Experiences

   https://doi.org/10.1109/iSES54909.2022.00067  

   Karam, Robert; Katkoori, Srinivas; Mozaffari-Kermani, Mehran (December 2022, 2022 IEEE International Symposium on Smart Electronic Systems (iSES))

   Practical, hands-on experience is an essential component of computer science and engineering education, especially in the cybersecurity domain. In this project, we are investigating techniques for improving student learning in such courses, first by developing a new hands-on hardware security course, then by testing the impact of gamification on student learning. The experiments utilize only inexpensive, open-source or freely-available software and hardware, and upon project completion, the modules themselves will also be made freely available online. Improving student learning in this critical area can have a wide-spread positive societal impact as we encourage students to have a security-first, secure-by-design mindset. 

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4. Structuring a Comprehensive Software Security Course Around the OWASP Application Security Verification Standard

   Elder, Sarah; Zahan, Nusrat; Kozarev, Val; Shu, Rui; Menzies, Tim; Williams, Laurie (May 2021, Proceedings of the International Conference on Software Engineering)

   null (Ed.)

   Lack of security expertise among software practitioners is a problem with many implications. First, there is a deficit of security professionals to meet current needs. Additionally, even practitioners who do not plan to work in security may benefit from an increased understanding of security. The goal of this paper is to aid software engineering educators in designing a comprehensive software security course by sharing an experience running a software security course for the eleventh time. Through all the eleven years of running the software security course, the course objectives have been comprehensive -- ranging from security testing, to secure design and coding, to security requirements to security risk management. For the first time in this eleventh year, a theme of the course assignments was to map vulnerability discovery to the security controls of the Open Web Application Security Project (OWASP) Application Security Verification Standard (ASVS). Based upon student performance on a final exploratory penetration testing project, this mapping may have increased students' depth of understanding of a wider range of security topics. The students efficiently detected 191 unique and verified vulnerabilities of 28 different Common Weakness Enumeration (CWE) types during a three-hour period in the OpenMRS project, an electronic health record application in active use. 

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5. Machine Learning in a Molecular Modeling Course for Chemistry, Biochemistry, and Biophysics Students

   https://doi.org/10.35459/tbp.2019.000140  

   Remington, Jacob M.; Ferrell, Jonathon B.; Zorman, Marlo; Petrucci, Adam; Schneebeli, Severin T.; Li, Jianing (June 2020, The Biophysicist)

   null (Ed.)

   ABSTRACT Recent advances in computer hardware and software, particularly the availability of machine learning (ML) libraries, allow the introduction of data-based topics such as ML into the biophysical curriculum for undergraduate and graduate levels. However, there are many practical challenges of teaching ML to advanced level students in biophysics majors, who often do not have a rich computational background. Aiming to overcome such challenges, we present an educational study, including the design of course topics, pedagogic tools, and assessments of student learning, to develop the new methodology to incorporate the basis of ML in an existing biophysical elective course and engage students in exercises to solve problems in an interdisciplinary field. In general, we observed that students had ample curiosity to learn and apply ML algorithms to predict molecular properties. Notably, feedback from the students suggests that care must be taken to ensure student preparations for understanding the data-driven concepts and fundamental coding aspects required for using ML algorithms. This work establishes a framework for future teaching approaches that unite ML and any existing course in the biophysical curriculum, while also pinpointing the critical challenges that educators and students will likely face. 

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