CS2 course projects can often be too prescriptive by telling students which algorithms or data structures are necessary to efficiently solve a given problem. Students may not fully understand why these algorithms or data structures were chosen, and they may not have an opportunity to empirically observe the impact of such design decisions. In 2019, we redesigned our CS2 course projects at North Carolina State University to help demonstrate the importance of critical-thinking and analysis activities when developing software. The objective of the redesigned course project is to connect computer science theory with software development practice by incorporating algorithm design and analysis, data structure selection, and experimental analysis as part of the software development lifecycle. For the project, students first create a design proposal that incorporates algorithm design and analysis, data structure selection, and other software design tasks. Next, students implement and test their software, while teaching staff test cases impose grade penalties for incorrect and/or inefficient implementations. Finally, students perform experimental analysis to empirically observe performance differences when using different data structures. Between January 2019 and May 2020, we collected end-of-course survey responses from 202 out of 536 students (37.7% response rate). Overall, 90.6% of respondents indicated that the project helped with understanding the importance of analysis and design when developing software. In this paper, we discuss our project activities in detail, along with lessons learned and suggestions for adopting similar projects in other CS2 courses.

We study the Max Partial $H$-Coloring problem: given a graph $G$, find the largest induced subgraph of $G$ that admits a homomorphism into $H$, where $H$ is a fixed pattern graph without loops. Note that when $H$ is a complete graph on $k$ vertices, the problem reduces to finding the largest induced $k$-colorable subgraph, which for $k=2$ is equivalent (by complementation) to Odd Cycle Transversal. We prove that for every fixed pattern graph $H$ without loops, Max Partial $H$-Coloring can be solved in $\{P_5,F\}$-free graphs in polynomial time, whenever $F$ is a threshold graph; in $\{P_5,{bull}\}$-free graphs in polynomial time; in $P_5$-free graphs in time $n^{\mathcal{O}(\omega(G))}$; and in $\{P_6,{1-subdivided claw}\}$-free graphs in time $n^{\mathcal{O}(\omega(G)^3)}$. Here, $n$ is the number of vertices of the input graph $G$ and $\omega(G)$ is the maximum size of a clique in $G$. Furthermore, by combining the mentioned algorithms for $P_5$-free and for $\{P_6,{1-subdivided claw}\}$-free graphs with a simple branching procedure, we obtain subexponential-time algorithms for Max Partial $H$-Coloring in these classes of graphs. Finally, we show that even a restricted variant of Max Partial $H$-Coloring is $\mathsf{NP}$-hard in the considered subclasses of $P_5$-free graphs if we allow loops on $H$.

The Department of Defense currently maintains a network known as the Defense Research Engineering Network (DREN), which provides various Department of Defense (DoD) sites across the nation connectivity to HPC resource centers. To ensure the security of the DREN system, a defense system known as the Cybersecurity Environment for Detection, Analysis, and Reporting (CEDAR) was created. CEDAR contains a variety of cybersecurity sensors, which constantly monitor and record real time network activity on the DREN. Over time, CEDAR has accumulated massive quantities of valuable cybersecurity data, which necessitates a form of automation in the process of reviewing this data. We propose the application of deep learning techniques to CEDAR data in an attempt to automatically detect potentially malicious activity in a more agile and adaptable manner. These deep learning techniques can be carried out in a high performance computing (HPC) environment, allowing for the rapid utilization of large amounts of data. Our most effective model is able to classify CEDAR alerts as malicious with an accuracy sufficient to greatly reduce human analyst workloads.

Situated learning theory supports engaging students with materials and resources that reflect professional standards and best practices. Starting with our introductory courses, we incorporate situated learning to support student engagement in software engineering practices and processes through the use of industrial strength open-source tools in several classes throughout the undergraduate computer science curriculum at NC State University. Additionally, these tools support several logistical and educational needs in computer science classrooms, including assignment submission systems and automated grading. In this tools paper, we present our Canary Framework for supporting software engineering practices through the use of Eclipse for development; GitHub for submission and collaboration; and Jenkins for continuous integration and automated grading. These tools are used in five of ten core courses by more than 3000 students over ten semesters. While the use of these tools in education is not unique, we want to share our model of using professional tools in a classroom setting and our experiences on how this framework can support multiple courses throughout the curriculum and at scale.

Students learn best in environments where they can meaningfully engage with materials that emulate real-world scenarios. Incorporating software engineering best practices and supporting tools in introductory courses provides students the opportunity to engage in course materials as a novice member of the profession. We support student engagement with industry tools to support software engineering best practices for tutorials, in-class labs, and programming projects. The goal of the research is to improve student learning, engagement in the course and profession, and retention through the use of software engineering practices and tools that introduce students to the software engineering profession. A prior study on the incorporation of in-class laboratories, supported with software engineering best practices, on linear data structures showed an increase in engagement, but did not show a difference on student learning when compared with active learning lectures. We are currently expanding the study by incorporating in-class laboratories across a full semester of a CS1.5 class at NC State University. The poster presents the preliminary results from Fall 2015.

Ideally, software engineering courses should adequately reflect real-world software development so that students obtain a better understanding and experience with practices and techniques used in industry. Our objective is to improve software engineering courses by incorporating best practices for automated software engineering and facilitating rapid feedback for students using an open source continuous integration framework for evaluating student software development. The open source Jenkins Continuous Integration Server is the core of our framework, which provides a consistent environment for building student projects, executing automated test cases, calculating code coverage, executing static analysis, and generating reports for students. By using continuous integration, a common tool in real-world software development, we can incorporate software engineering best practices, introduce students to continuous integration in practice, and provide formative feedback to students throughout the software development lifecycle. We found that 76% or more of students in each of the classes that deploy our framework reported that using Jenkins increased their productivity, and that 84% or more of students in each of the classes reported that using Jenkins increased their code quality.

Forensic analysis of software log files is used to extract user behavior profiles, detect fraud, and check compliance with policies and regulations. Software systems maintain several types of log files for different purposes. For example, a system may maintain logs for debugging, monitoring application performance, and/or tracking user access to system resources. The objective of my research is to develop and validate a minimum set of log file attributes and software security metrics for user nonrepudiation by measuring the degree to which a given audit log file captures the data necessary to allow for meaningful forensic analysis of user behavior within the software system. For a log to enable user nonrepudiation, the log file must record certain data fields, such as a unique user identifier. The log must also record relevant user activity, such as creating, viewing, updating, and deleting system resources, as well as software security events, such as the addition or revocation of user privileges. Using a grounded theory method, I propose a methodology for observing the current state of activity logging mechanisms in healthcare, education, and finance, then I quantify differences between activity logs and logs not specifically intended to capture user activity. I will then propose software security metrics for quantifying the forensic-ability of log files. I will evaluate my work with empirical analysis by comparing the performance of my metrics on several types of log files, including both activity logs and logs not directly intended to record user activity. My research will help software developers strengthen user activity logs for facilitating forensic analysis for user nonrepudiation.