Summary of the Fourth International Workshop on Deep Learning for Testing and Testing for Deep Learning (DeepTest 2023)
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
Deep Learning (DL) techniques help software developers thanks to their ability to learn from historical information which is useful in several program analysis and testing tasks (e.g., malware detection, fuzz testing, bug-finding, and type-checking). DL-based software systems are also increasingly adopted in safety-critical domains, such as autonomous driving, medical diagnosis, and aircraft collision avoidance systems. In particular, testing the correctness and reliability of DL-based systems is paramount, since a failure of such systems would cause a significant safety risk for the involved people and/or environment. The 4th International Workshop on Deep Learning for Testing and Testing for Deep Learning (DeepTest 2023) was co-located with the 45th International Conference on Software Engineering (ICSE), with the goal of targeting research at the intersection of software engineering and deep learning and devise novel approaches and tools to ensure the interpretability and dependability of software systems that depends on DL components.
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 | 0.533 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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