AI-Generated Bug Reports

Updated 30 November 2025

AI-generated bug reports are structured defect records produced by leveraging advanced NLP, LLMs, and software analysis to extract key fields like steps-to-reproduce and error logs.
They employ multi-stage processes—including preprocessing, incremental extraction, and continuous feedback loops using models like Qwen 2.5 and Mistral 7B—to enhance report completeness and clarity.
Empirical evaluations highlight improvements such as up to a 75% reduction in time-to-fix and increased reproduction rates from 30% to 70%, demonstrating significant gains in debugging efficiency.

AI-generated bug reports are formal, structured records of software defects produced or refined wholly or in part by artificial intelligence systems, commonly leveraging LLMs, statistical NLP, or structured code analysis tools. These systems automate or augment the entire reporting pipeline, from collecting raw user input and log traces to synthesizing comprehensive, developer-ready bug reports, including structured fields such as steps-to-reproduce (S2R), observed and expected behaviors, and runtime evidence. The recent literature details systems that generate, enhance, classify, and reproduce bug reports, encapsulating a broad spectrum of both natural-language understanding and software engineering automation.

1. Automated Bug Report Generation: Architectures and Techniques

AI-driven bug report generation encompasses a variety of architectures and core methodologies. At the data-ingestion layer, systems such as BugBlitz-AI (Yao et al., 17 May 2024) and EBug (Fazzini et al., 2022) consume unprocessed test outputs, user summaries, error logs, or live user dialogues. These inputs may be further normalized, lexically parsed, and enriched with metadata (e.g., test environment, execution context).

Contemporary frameworks adopt a modular, multi-stage approach:

Preprocessing: Applies NLP normalization, pattern matching, and extraction of core attributes such as error messages and stack traces (Yao et al., 17 May 2024).
Incremental Information Capture: Models like EBug perform real-time mapping of free-form S2R input into canonical actions via dependency parsing, static/dynamic GUI graphs, and fastText embedding similarity (Fazzini et al., 2022).
LLM-based Field Inference and Synthesis: Fine-tuned instruction models (Qwen 2.5, Mistral, Llama 3.2) or proprietary LLMs are prompted to complete or rewrite reports into standard templates, filling in missing fields with pretrained knowledge and template memorization (Acharya et al., 26 Apr 2025).
Continuous Feedback and Iteration: Interactive agents detect missing fields, request clarifications, and iteratively refine the bug report structure via conversational AI (Torun et al., 9 Oct 2025).

Parameter-efficient fine-tuning, especially with Low-Rank Adaptation (LoRA) applied specifically to attention layers, is routine for domain adaptation in LLM-based modules (Acharya et al., 26 Apr 2025, Yao et al., 17 May 2024).

2. Task Specialization: Error Analysis, Field Extraction, and Duplication Removal

AI-synthesized bug reporting often decomposes the pipeline into specialized LLM-driven subtasks:

Root Cause Identification: Discriminates root errors in cascaded logs using instruction-tuned models such as DeepSeek-Coder-7b-instruct (Yao et al., 17 May 2024).
Bug/Environment Classification: Classifies root errors as actionable software bugs or environmental test failures, leveraging pattern-matching and chain-of-thought prompting, typically with models like Mistral-7B (Yao et al., 17 May 2024).
Concise Summarization: Synthesizes summary and detailed description fields for tracker ingestion (e.g., Jira), often using prompt chaining NLG on instruction-tuned LLMs, like CodeLlama-7b-Instruct (Yao et al., 17 May 2024).
Duplicate Detection: Compares generated reports against existing issues to suppress redundancy, relying on semantic similarity prompts (Yao et al., 17 May 2024).

For field extraction from user or chat input, systems utilize transfer learning (e.g., TextCNN+transfer for sentence classification) and paragraph/sentence-level NLP to label observed behavior (OB), expected behavior (EB), and S2Rs (Shi et al., 2022, Acharya et al., 26 Apr 2025).

3. Data Augmentation, Template Robustness, and Domain Transfer

Data augmentation is critical for robust learning in bug report NLP:

Synthetic Generation: Token-level (replace/insert/swap/delete), dictionary- and code-based token manipulation, and backtranslation artefacts are synthesized for each labeled section (OB, EB, S2R, stacktrace, code snippet) (Ciborowska et al., 2023).
Semantic and Structural Filtering: Augmentation candidates are retained only if key invariants (field labels, code references) are unbroken (Ciborowska et al., 2023).
Recombination and Balancing: Augmented segments are reassembled with random component drop or reordering, and corpus balancing ensures rare classes are not over- or under-represented during training (Ciborowska et al., 2023).
Template Generalization: Instruction-finetuned LLMs (notably Qwen 2.5) exhibit strong cross-project transfer capabilities, maintaining ~70% CTQRS on previously unseen projects (Acharya et al., 26 Apr 2025).

4. Automated Bug Reproduction and Validation

Bug reproduction – executing the described S2R to observe the defect – is increasingly automated via LLMs:

Whole-Report Reasoning: Systems such as REBL (Wang et al., 6 Jul 2024) bypass rigid S2R entity extraction, instead leveraging the entire textual report plus live UI context in iterative prompt-feedback cycles with GPT-4.
Feedback-Driven Execution: Automated agents iteratively propose GUI actions, receive UI state feedback, and continue or exit based on symptom detection (e.g., presence of crash dialogs or functional misbehavior) (Wang et al., 6 Jul 2024).
Empirical Performance: REBL reproduces 90.63% of Android bugs from user reports (94.52% crash, 78.26% non-crash) with an average time of ~75s per report, substantially exceeding prior methods in both success and speed (Wang et al., 6 Jul 2024).

5. Evaluation Metrics and Empirical Benchmarks

Common metrics for assessing AI-generated bug reports include rule-based completeness/quality scores (CTQRS; max 17, normalized), ROUGE-1 (unigram overlap), METEOR (recall-weighted F₁), SBERT cosine similarity, classification F₁ for key fields (S2R, OB/EB/AB), and task-specific performance (time to reproduce, time to fix, reproduction and acceptance rates) (Acharya et al., 26 Apr 2025, Zhao et al., 6 Oct 2024, Torun et al., 9 Oct 2025). The most successful methods report:

Model/System	In-domain CTQRS (%)	Cross-proj. CTQRS (%)	S2R F₁	Time-to-fix/resp. gains
Qwen 2.5 (LoRA)	77	70	0.76	--
Mistral 7B (LoRA)	71	64	0.71	--
Llama 3.2B	63	55	0.65	--
ChatGPT-4o (3-shot)	75	73	0.70	--
BugBlitz-AI (precision)	--	--	--	69.3% precision, 100% recall
REBL (reproduction rate)	90.63%	--	--	5–7× speedup over SoTA
LLM-powered triage (Torun et al., 9 Oct 2025)	--	--	--	0.90 F₁ (classification), ~75% faster end-to-end

Fine-tuned, open LLMs demonstrate strong in-domain and cross-project performance, with LoRA and prompt engineering boosting both recall and precision.

6. Integrated, Multi-Agent Bug Tracking Pipelines

LLM-powered bug tracking frameworks embed generative models throughout the defect lifecycle (Torun et al., 9 Oct 2025):

Report Intake: Chatbot front-ends automatically elicit clarifying questions, then synthesize structured bug reports via specialized enhancement agents.
Automated Reproduction: LLM-driven reproduction agents synthesize and execute test scripts, iteratively refining S2Rs upon failure (feedback loop).
Triage and Classification: Zero-/few-shot classification models filter invalid/no-code-fix reports.
AI-Assisted Localization and Patch Generation: Information retrieval and LLM agents localize faults, and code-generation models propose candidate patches scored by hybrid metrics.
Human-in-the-Loop Oversight: All phases include manual fallback, with explainability, accountability, and safety as primary concerns.

System-wide evaluations demonstrate a reduction in average time-to-fix by up to 75%, a doubling in patch-acceptance rates, and marked improvements in first-try reproduction success; for example, first-try reproduction rates increase from 30% (manual) to 70% (AI-augmented) (Torun et al., 9 Oct 2025).

7. Limitations, Open Challenges, and Future Directions

Persisting limitations include:

Accumulated Multi-agent Errors: Failures in early phases may propagate, degrading downstream performance (Torun et al., 9 Oct 2025).
Accountability and Explainability: LLM and agent pipelines lack transparency; maintaining human oversight is essential.
Domain Adaptation and Robustness: LLMs trained on general corpora sometimes misclassify domain-specific reports; rare event and edge-case handling require further paper.
Economic and Privacy Considerations: API usage, fine-tuning costs, and data sensitivity present adoption barriers.
Dataset Availability: There is a dearth of openly annotated, full-lifecycle bug tracker datasets.

Research continues on formalizing benchmarks, optimizing LoRA and quantization for resource-constrained environments, and integrating multimodal data (images, logs, traces) to fortify bug report synthesis and validation (Acharya et al., 26 Apr 2025, Torun et al., 9 Oct 2025).

In summary, AI-generated bug reports—comprising both fully synthesized and human-refined outputs—are now central to modern, efficient, and automated bug tracking systems. Recent advances demonstrate robust, template-driven field extraction, whole-report reasoning for automated reproduction, and impressive in-domain and cross-project performance, albeit with open challenges in error propagation, transparency, and real-world deployment scalability. The ecosystem continues to benefit from tight integration of LLMs, precise data augmentation, feedback-driven automation, and multi-agent orchestration throughout the bug-tracking lifecycle.