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  • 1.
    Abrahamsson, Sara
    et al.
    Linköping University, Department of Computer and Information Science.
    Andersson, Frida
    Linköping University, Department of Computer and Information Science.
    Jaldevik, Albin
    Linköping University, Department of Computer and Information Science.
    Nyrfors, Frans
    Linköping University, Department of Computer and Information Science.
    Jareman, Erik
    Linköping University, Department of Computer and Information Science.
    Kröger, Oscar
    Linköping University, Department of Computer and Information Science.
    Tjern, Martin
    Linköping University, Department of Computer and Information Science.
    TopQ - a web-based queuing application: A case study in developing a queuing application for students and tutors with focus on navigability and design2021Independent thesis Basic level (degree of Bachelor), 12 credits / 18 HE creditsStudent thesis
    Abstract [en]

    Students’ learning processes can be affected negatively by long waiting times to get assistance on lesson- and lab-sessions. Studies show that digital queuing systems decrease the waiting time. Thus, the purpose of this report is to investigate how to design a web-based queuing application to achieve a high perceived usability for students and tutors. Especially based on navigability and design which in accordance with research in the area has a direct impact on the usability. To achieve a high perceived usability the application was developed iteratively. In the first version the implemented functionality was built upon the result from the feasibility study combined with research in the area. After a set of user evaluations, changes from the first version were implemented to further improve the perceived usability. Lastly, another set of evaluations were performed to confirm the improvement in the final version. The results showed that the first version of the system was perceived as 84 out of 100 on the System Usability Scale (SUS) and the final version as 88 out of 100, an improvement by four units. Uniform design, no irrelevant functionality, placing buttons in conspicuous positions and having double checks to “dangerous actions” all seem to be factors contributing to the navigability, desirability and thus the usability on a queuing-application.

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  • 2.
    Andersson, Frida
    et al.
    Linköping University, Department of Computer and Information Science.
    Öberg, Albin
    Linköping University, Department of Computer and Information Science.
    Predicting Vulnerabilities in Third Party Open-Source Software using Data Mining and Machine Learning Techniques2023Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    In recent years, the use of third-party open source software (OSS) has increased significantly, making software security a critical concern. Predicting vulnerabilities in OSS can be a daunting task due to its complexity and the large volume of code involved. In this report, the use of data mining techniques and machine learning models to predict vulnerabilities in third party OSS is explored. The data used in this study was collected from GitHub repositories, where each repository consists of different package features fetched through the GitHub API. Repositories with reported vulnerabilities, CVEs, were then mapped to the corresponding vulnerability in the National Vulnerability Database (NVD).

    The study used data mining techniques to analyze a large dataset with data from third-party OSS, and identified patterns and relationships between GitHub repository features and reported vulnerabilities. The dataset consisted of over 30 000 instances of OSS packages. Furthermore, the study employed various machine learning models to predict known vulnerabilities. The result showed that the best machine learning mode had an accuracy of 91,7\% in predicting vulnerabilities in third-party OSS, and a precision of 0.90 for the positive class, representing repositories with reported CVEs.

    Furthermore, the study revealed relationships between GitHub repository features and vulnerabilities. For instance, the analysis uncovered that the number of stars and forks has the highest impact on the predictions performed by the machine learning models. This finding enables more efficient detection of vulnerabilities in third-party OSS, thereby enhancing the overall security of digital systems. Overall, this study provides an approach for vulnerability prediction in third-party OSS and sheds light on important relationships between GitHub features and reported CVEs that were previously unknown.

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    bilaga
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