Generated from prompt:
Create a 15–18 slide modern AI/ML-themed presentation for a research proposal DRC titled 'Intelligent Bug Triage using Machine Learning for Agile Teams'. Include these sections: 1. Title Slide – project title, student name, supervisor, department, university 2. Introduction – background of agile issue tracking and triage challenges 3. Problem Statement – manual triage limitations (time, scalability, inconsistency) 4. Research Questions – RQ1 (Accuracy), RQ2 (Feature Importance), RQ3 (Explainability) 5. Research Objectives – target accuracy, model comparisons, explainability goals, prototype 6. Research Significance – practical benefits, automation impact 7. Literature Review Overview – evolution from classic ML to transformers 8. Literature Review (Table Summary) – major authors, methods, datasets, gaps 9. Identified Research Gaps – data size, model limitations, explainability, integration 10. Proposed Methodology – stepwise plan (dataset, preprocessing, modeling, evaluation) 11. Dataset Details – sources, attributes, split 12. Model Development – classical ML vs DistilBERT, explainability (SHAP) 13. Evaluation Metrics – Top-K accuracy, precision, recall, etc. 14. Expected Results – comparative accuracy table (Classical ML vs DistilBERT) 15. System Prototype – Jira/GitHub integration overview 16. Timeline – 6-month research plan 17. Expected Contributions – academic & industrial relevance 18. References / Acknowledgement slide. Style: Modern tech theme with clean visuals, subtle AI icons, blue-cyber color palette, clear readable text, professional layout.
18-slide research proposal on ML-driven bug triage for agile teams. Covers challenges, RQs/objectives, lit review gaps, DistilBERT+SHAP methodology, Jira prototype, >85% accuracy goals, timeline, and
This title slide features the project title "Intelligent Bug Triage using Machine Learning for Agile Teams." The subtitle lists the student name, supervisor name, department, and university.
[Student Name], [Supervisor Name], [Department], [University]
Source: Title slide for DRC research proposal presentation
Agile methodologies enable rapid sprints and continuous integration, but teams face overwhelming daily volumes of bug reports. Manual triage delays priority assignment, hindering overall agile velocity and responsiveness.
The current manual triage process is time-consuming, poorly scalable for growing teams, and results in inconsistent prioritization. This leads to delays in issue resolution and overwhelms agile development workflows.
The slide titled "Research Questions" lists three key inquiries in bullet form. They cover model accuracy for bug triage (RQ1), key feature importances (RQ2), and explainability of ML decisions in agile contexts (RQ3).
This slide outlines research objectives to achieve over 85% accuracy in automated bug triage and compare classical ML models against DistilBERT performance. It also targets providing SHAP explainability for predictions and developing a Jira/GitHub integration prototype.
This research automates bug triage to speed up agile cycles and cut developer workload. It boosts software quality, classification accuracy, team productivity, and scalability.
This slide serves as the section header for Section 07: Literature Review Overview. It highlights the progression from classic ML methods (SVM, RF) to deep learning and transformers for bug report classification.
07
From Classic ML (SVM, RF) to Deep Learning and Transformers for Bug Report Classification
The "Literature Review Summary" slide presents a table reviewing four studies on bug classification, listing authors, methods (TF-IDF+SVM, Random Forest, BERT, Naive Bayes), datasets (Eclipse, Mozilla, Jira), and gaps (scalability, explainability, integration, data size). It highlights limitations in prior work to contextualize the current research.
{ "headers": [ "Authors", "Methods", "Datasets", "Gaps" ], "rows": [ [ "Zhou et al.", "TF-IDF + SVM", "Eclipse", "Scalability" ], [ "Youm et al.", "Random Forest", "Mozilla", "Explainability" ], [ "Zhang et al.", "BERT", "Jira", "Integration" ], [ "Lamkanfi et al.", "Naive Bayes", "Eclipse", "Data size" ] ] }
The "Identified Research Gaps" slide outlines key challenges in bug triage research. It highlights limited large-scale datasets, poor model generalization across projects, lack of explainable AI, and inadequate integration with tools like Jira.
The slide outlines a four-phase methodology workflow for bug report analysis: Dataset Collection from sources like Eclipse and GitHub, Preprocessing with NLTK and spaCy, Modeling using Scikit-learn and DistilBERT, and Evaluation with metrics like Top-K accuracy and SHAP. Each phase details key activities and corresponding tools for implementation.
{ "headers": [ "Phase", "Key Activities", "Methods/Tools" ], "rows": [ [ "1. Dataset Collection", "Gather bug reports from public sources", "Eclipse, Mozilla, Jira/GitHub datasets" ], [ "2. Preprocessing", "Tokenization, cleaning, feature engineering", "NLTK, spaCy, tokenizers" ], [ "3. Modeling", "Train Classical ML and DistilBERT models", "Scikit-learn, Hugging Face Transformers" ], [ "4. Evaluation & Explainability", "Performance metrics and model interpretation", "Top-K accuracy, Precision/Recall, SHAP" ] ] }
Source: DRC Research Proposal: Intelligent Bug Triage using Machine Learning for Agile Teams
The slide details a dataset sourced from Jira issue trackers and GitHub repositories, featuring real-world agile team bug reports for authentic triage scenarios. It includes attributes like summary text and severity levels (low, medium, high, critical), split as 70%/15%/15% for train/validation/test to ensure reliable model training.
| Data Sources | Attributes & Split |
|---|
| • Jira issue tracker
Real-world agile team bug reports and issues for authentic triage scenarios. | • Summary (text description)
Train/Validation/Test: 70%/15%/15% Ensures reliable model training and testing. |
The slide on Model Development contrasts classical ML models like Random Forest and SVM, which use engineered features from bug descriptions for interpretable classification but struggle with complex text semantics. It also highlights DistilBERT—a fine-tuned lightweight transformer for advanced NLP that captures contextual embeddings—paired with SHAP for explainable triage via feature contribution visualizations.
| Classical ML (RF, SVM) | DistilBERT + SHAP |
|---|---|
| Traditional models: Random Forest and Support Vector Machines. Uses engineered features from bug descriptions, metadata for classification. Simple, interpretable, but limited on complex text semantics. | Fine-tuned lightweight transformer for advanced NLP. Captures contextual embeddings from issues. SHAP ensures explainability by visualizing feature contributions to triage decisions. |
The "Evaluation Metrics" slide outlines five key metrics for bug recommendation systems: Top-K Accuracy, Precision@K, Recall@K, F1-Score, and Mean Reciprocal Rank (MRR). These measure aspects like correct predictions in top K results, relevance of retrieved bugs, their harmonic mean, and ranking effectiveness.
The "Expected Results" slide features a table comparing Classical ML and DistilBERT on key metrics. DistilBERT outperforms Classical ML across all, achieving 90% accuracy and F1-score, 88% precision, and 92% recall versus 80%, 80%, 78%, and 82%.
{ "headers": [ "Metric", "Classical ML", "DistilBERT" ], "rows": [ [ "Accuracy", "80%", "90%" ], [ "Precision", "78%", "88%" ], [ "Recall", "82%", "92%" ], [ "F1-Score", "80%", "90%" ] ] }
The "System Prototype" slide presents a visual prototype integrating real-time Jira/GitHub issue synchronization and ML-powered automated bug triage with SHAP explainability. It also features a dashboard delivering actionable insights for triage decisions.
Source: Wikipedia
The slide presents a 6-month research timeline for bug triage using ML models. It covers data collection and literature review (Months 1-2), model development/training (Months 3-4), evaluation/analysis (Month 5), and prototype/reporting (Month 6).
Months 1-2: Data Collection & Literature Review Gather datasets from Jira/GitHub and conduct comprehensive literature review on bug triage. Months 3-4: Model Development & Training Implement classical ML models and DistilBERT, perform preprocessing and training. Month 5: Evaluation & Analysis Assess models with Top-K accuracy, precision, recall, and SHAP explainability techniques. Month 6: Prototype & Final Reporting Develop Jira/GitHub integration prototype and compile research report.
The slide lists expected contributions, including a novel DistilBERT+SHAP framework for bug triage that achieves superior accuracy over classical ML baselines and enhanced explainability via SHAP analysis. It also highlights an agile automation prototype for Jira/GitHub and bridges research gaps in industrial integration.
The slide lists key references on bug prediction, ML triage, DistilBERT, and SHAP. It acknowledges the supervisor, Agile AI Lab team, NSF funding, and invites questions for collaboration on smarter triage.
**Key References:
Acknowledgements:
Thank you! Questions? Let's collaborate on smarter triage.
Source: Intelligent Bug Triage using Machine Learning for Agile Teams
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