One Sentence Can Kill the Bug: Auto-Replay Mobile App Crashes From One-Sentence Overviews
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
Crash reports play a crucial role in software maintenance as they inform developers about the issues encountered in mobile applications. Developers must reproduce the reported crash before fixing it, which is extremely time-consuming and tedious. Existing studies have focused on automatic crash reproduction with step-by-step instructions. However, a non-neglectable portion of crash reports only provides a one-sentence overview, which merely describes the final crash-triggering action. These reports require developers to invest more effort in understanding and fixing the issues while existing techniques cannot handle them due to the lack of step-by-step guidance, thus calling for a greater need for automatic support. Leveraging the capability of Large Language Models (LLMs) in combining acting and reasoning, we propose ReActDroid, an automated approach to reproduce mobile application crashes directly from the crash overview. ReActDroid utilizes ReAct prompting to augment the app-specific knowledge and exploration history, enabling the LLM to derive the necessary steps for triggering the crash from a comprehensive and historical perspective. We evaluate ReActDroid on 102 crash reports from 69 popular Android apps and successfully reproduce 57.8% of the crashes, surpassing the performance of state-of-the-art baselines by 69% to 321%. Besides, the average reproducing time is 51.8 seconds, outperforming the baselines by 73% to 949%. We also evaluate the usefulness of ReActDroid with promising results.
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 | 0.000 | 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 | 0.001 | 0.000 |
| 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