Evaluating Multi-Agent AI Systems for Automated Bug Detection and Code Refactoring
Bibliographic record
Abstract
This paper evaluates multi-agent AI systems for automating software bug detection and code refactoring. We design a cooperative architecture in which specialized agents—static-analysis, test-generation, root-cause, and refactoring—coordinate via a planning agent to propose, verify, and apply patches. The system integrates LLM-based reasoning with conventional program analysis to reduce false positives and preserve behavioral equivalence. We implement a reference pipeline on opensource Python/Java projects and compare against single-agent and non-LLM baselines. Results indicate higher fix precision and refactoring quality, with reduced developer review time, especially on multi-file defects and design-smell cleanups. We report ablations on agent roles, verification depth, and communication cost, and discuss failure modes (spec ambiguities, overrefactoring, flaky tests). A reproducible workflow, dataflow diagram, and flowcharts are provided to support replication. Our findings suggest that disciplined, verifiable agent orchestration is a practical path to safer, more scalable automated maintenance in modern codebases.
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.
How this classification was reachedexpand
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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".