Report on the 5th Workshop on Human Centric Software Engineering & Cyber Security (HCSE&CS 2024)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Humans play multifaceted roles in the lifecycle of software systems, from creation and design to coding, testing, and usage. Traditionally, software engineering and cyber security research have prioritized technical aspects such as functions, data, and processes, while neglecting crucial human factors. Human-centric software engineering and cyber security prioritizes the human element, ensuring usability, accessibility, and trust are central to design and implementation. The InternationalWorkshop on Human Centric Software Engineering & Cyber Security (HCSE&CS) aims to create a forum to discuss enhanced theories, models, tools, and practices that support next-generation human-centric approaches in software engineering and cyber security. The fifth edition of the HCSE&CS Workshop was held on 28 October 2024, alongside the 39th IEEE/ACM International Conference on Automated Software Engineering (ASE 2024) in Sacramento, California, United States. It brought together experts to discuss not only traditional human-centric software engineering and cybersecurity challenges but also the evolving impact of large language models (LLMs) on software development and security. This report outlines the workshop's motivation and objectives and summarizes the presentations and discussions that took place during this event.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it