Effectiveness of ChatGPT for Static Analysis: How Far Are We?
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
This paper conducted a novel study to explore the capabilities of ChatGPT, a state-of-the-art LLM, in static analysis tasks such as static bug detection and false positive warning removal. In our evaluation, we focused on two types of typical and critical bugs targeted by static bug detection, i.e., Null Dereference and Resource Leak, as our subjects. We employ Infer, a well-established static analyzer, to aid the gathering of these two bug types from 10 open-source projects. Consequently, our experiment dataset contains 222 instances of Null Dereference bugs and 46 instances of Resource Leak bugs. Our study demonstrates that ChatGPT can achieve remarkable performance in the mentioned static analysis tasks, including bug detection and false-positive warning removal. In static bug detection, ChatGPT achieves accuracy and precision values of up to 68.37% and 63.76% for detecting Null Dereference bugs and 76.95% and 82.73% for detecting Resource Leak bugs, improving the precision of the current leading bug detector, Infer by 12.86% and 43.13% respectively. For removing false-positive warnings, ChatGPT can reach a precision of up to 93.88% for Null Dereference bugs and 63.33% for Resource Leak bugs, surpassing existing state-of-the-art false-positive warning removal tools.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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