Context: Computer science, even now, attracts a small number of women, and the proportion of women in the field decreases through advancing career stages. Consequently, few women progress to Ph.D. studies in computer science after completing master’s studies. Empowering women at this stage in their careers is essential, not just for equality reasons, but to unlock untapped potential for society, industry and academia. Objective: This paper aims to identify students’ career assumptions and information related to Ph.D. studies focused on gender-based differences. We propose a program to inform female master students about Ph.D. studies that explains the process, clarifies misconceptions, and alleviates concerns. Method: An extensive survey was conducted to identify factors that encourage and discourage students from undertaking Ph.D. studies. The analysis identified statistically significant differences between those who undertook Ph.D. studies and those who did not, as well as statistically significant gender differences. A catalogue of questions to initiate discussions with potential Ph.D. students which allowed them to explore these factors was developed. These were structured into a Women’s Career Lunch program where students can explore and discuss the benefits of Ph.D. study. Results: Encouraging factors towards Ph.D. study include interest and confidence in research arising from a research involvement during earlier studies; enthusiasm for and self-confidence in computer science in addition to an interest in an academic career; encouragement from external sources; and a positive perception towards Ph.D. studies which can involve achieving personal goals. Discouraging factors include uncertainty and lack of knowledge of the Ph.D. process, a perception of lower job flexibility, and the requirement for long-term commitment. Gender differences highlighted that female students who pursue a Ph.D. have less confidence in their technical skills than males but a higher preference for interdisciplinary areas. Female students are less inclined than males to perceive the industry as offering better job opportunities and more flexible career paths than academia. Conclusions: The insights collected from the survey facilitated the development of a questions catalogue structured into the Women Career Lunch program to help students make a more informed decision concerning whether they should pursue a Ph.D. in computer science. Localised versions of this program, in 8 languages, were created to support its adoption in different countries and assist in mitigating the female under-representation challenge.

Plagiarism is a significant challenge in computer science education. Thus, tool-based approaches are widely used to combat software plagiarism. However, especially due to the recent rise of automated obfuscation via algorithmic or AIbased techniques, these tools face difficulties due to increasingly sophisticated obfuscation techniques. To address this challenge, we present a novel defense mechanism against automated obfuscation attacks. This mechanism iteratively merges matching program subsequences to counteract the effects of the obfuscation. Our approach is language-independent, attack-agnostic, and integrates well into state-of-the-art software plagiarism detectors. The evaluation based on five real-world datasets indicates that our approach not only provides broader resilience against algorithmic and AI-based obfuscation attacks than the state-of-the-art but also improves the detection of fully AI-generated programs.

Serverless computing has emerged as a prominent paradigm, with a significant adoption rate among cloud customers. While this model offers advantages such as abstraction from the deployment and resource scheduling, it also poses limitations in handling complex use cases due to the restricted nature of individual functions. Serverless workflows address this limitation by orchestrating multiple functions into a cohesive application. However, existing serverless workflow platforms exhibit significant differences in their programming models and infrastructure, making fair and consistent performance evaluations difficult in practice. To address this gap, we propose the first serverless workflow benchmarking suite SeBS-Flow, providing a platform-agnostic workflow model that enables consistent benchmarking across various platforms. SeBS-Flow includes six real-world application benchmarks and four microbenchmarks representing different computational patterns. We conduct comprehensive evaluations on three major cloud platforms, assessing performance, cost, scalability, and runtime deviations. We make our benchmark suite open-source, enabling rigorous and comparable evaluations of serverless workflows over time. Implementation: https://github.com/spcl/serverless-benchmarks Artifact: https://github.com/spcl/sebs-flow-artifact

While software plagiarism detectors have been used for decades, the assumption that evading detection requires programming proficiency is challenged by the emergence of automated plagiarism generators. These generators enable effortless obfuscation attacks, exploiting vulnerabilities in existing detectors by inserting statements to disrupt the matching of related programs. Thus, we present a novel, language-independent defense mechanism that leverages program dependence graphs, rendering such attacks infeasible. We evaluate our approach with multiple real-world datasets and show that it defeats plagiarism generators by offering resilience against automated obfuscation while maintaining a low rate of false positives.

Aggregated HPC resources have rigid allocation systems and programming models which struggle to adapt to diverse and changing workloads. Consequently, HPC systems fail to efficiently use the large pools of unused memory and increase the utilization of idle computing resources. Prior work attempted to increase the throughput and efficiency of supercomputing systems through workload co-location and resource disaggregation. However, these methods fall short of providing a solution that can be applied to existing systems without major hardware modifications and performance losses. In this paper, we improve the utilization of supercomputers by employing the new cloud paradigm of serverless computing. We show how serverless functions provide fine-grained access to the resources of batchmanaged cluster nodes. We present an HPC-oriented Functionas-a-Service (FaaS) that satisfies the requirements of high-performance applications. We demonstrate a software resource disaggregation approach where placing functions on unallocated and underutilized nodes allows idle cores and accelerators to be utilized while retaining near-native performance.Full Paper Version: https://arxiv.org/abs/2401.10852HPC FaaS Implementation: https://github.com/spcl/rFaaS

The many configuration options of modern applications make it difficult for users to select a performance-optimal configuration. Performance models help users in understanding system performance and choosing a fast configuration. Existing performance modeling approaches for applications and configurable systems either require a full-factorial experiment design or a sampling design based on heuristics. This results in high costs for achieving accurate models. Furthermore, they require repeated execution of experiments to account for measurement noise. We propose Performance-Detective, a novel code analysis tool that deduces insights on the interactions of program parameters. We use the insights to derive the smallest necessary experiment design and avoiding repetitions of measurements when possible, significantly lowering the cost of performance modeling. We evaluate Performance-Detective using two case studies where we reduce the number of measurements from up to 3125 to only 25, decreasing cost to only 2.9% of the previously needed core hours, while maintaining accuracy of the resulting model with 91.5% compared to 93.8% using all 3125 measurements.