This document contains the complete user story backlog for the IntelliTag tag suggestion system, organized by epic and priority.
As a data engineer I want to collect and load Stack Overflow questions data So that we have a representative dataset for model training
Acceptance Criteria:
- Load data from Stack Exchange Data Explorer exports
- Parse CSV format with Title, Body, Tags, and metadata
- Handle encoding issues (UTF-8)
- Log ingestion statistics (row count, null values)
Priority: P0 (Critical) Story Points: 3
As a data scientist I want to extract clean text from HTML-formatted question bodies So that the NLP models receive properly formatted input
Acceptance Criteria:
- Remove all HTML tags using BeautifulSoup
- Preserve code snippets content (without formatting)
- Handle malformed HTML gracefully
- Maintain text structure (paragraphs, lists)
Priority: P0 (Critical) Story Points: 2
As a data scientist I want to tokenize text into meaningful units So that feature extraction can process individual tokens
Acceptance Criteria:
- Split text on whitespace and punctuation
- Handle special characters in programming context (+, #, .)
- Preserve compound technical terms (e.g., "scikit-learn")
- Support both word-level and sentence-level tokenization
Priority: P0 (Critical) Story Points: 3
As a data scientist I want to filter out non-informative words So that models focus on meaningful content
Acceptance Criteria:
- Remove standard English stop words
- Preserve technical stop words that have meaning (e.g., "null", "void")
- Remove common punctuation artifacts
- Filter words shorter than 3 characters (configurable)
Priority: P1 (High) Story Points: 2
As a data scientist I want to reduce words to their base form So that variations of the same word are treated consistently
Acceptance Criteria:
- Apply WordNet lemmatization
- Handle technical terms correctly
- Preserve proper nouns and library names
- Support batch processing for efficiency
Priority: P1 (High) Story Points: 2
As a data scientist I want to extract individual tags from the tag string format So that we have clean labels for supervised learning
Acceptance Criteria:
- Parse
<tag1><tag2>format into list - Handle edge cases (empty tags, malformed strings)
- Create tag frequency analysis
- Support multi-label format for model training
Priority: P0 (Critical) Story Points: 1
As a data scientist I want to create BoW representations of questions So that we have a baseline feature set for classification
Acceptance Criteria:
- Implement TF-IDF vectorization
- Configure vocabulary size (max features)
- Support n-gram ranges (unigrams, bigrams)
- Save vectorizer for inference
Priority: P0 (Critical) Story Points: 3
As a data scientist I want to create Word2Vec-based document embeddings So that we capture semantic similarity between questions
Acceptance Criteria:
- Train or load pre-trained Word2Vec model
- Implement document embedding (average/weighted)
- Handle out-of-vocabulary words
- Evaluate embedding quality
Priority: P1 (High) Story Points: 5
As a data scientist I want to generate BERT-based contextual embeddings So that we capture deep semantic understanding
Acceptance Criteria:
- Load pre-trained BERT model (bert-base-uncased)
- Implement text truncation strategy (512 tokens)
- Extract [CLS] token embeddings
- Support batch processing for efficiency
Priority: P1 (High) Story Points: 5
As a data scientist I want to create USE-based sentence embeddings So that we have efficient semantic representations
Acceptance Criteria:
- Load TensorFlow Hub USE model
- Generate 512-dimensional embeddings
- Handle long texts appropriately
- Benchmark inference speed
Priority: P1 (High) Story Points: 3
As a data scientist I want to discover latent topics in questions So that we can enhance tag suggestions with topic information
Acceptance Criteria:
- Train LDA model with configurable topics (5-20)
- Evaluate coherence scores
- Visualize topic distributions
- Map topics to common tags
Priority: P2 (Medium) Story Points: 5
As a data scientist I want to train a classifier that predicts multiple tags So that questions receive comprehensive tag suggestions
Acceptance Criteria:
- Implement multi-label classification pipeline
- Support multiple algorithms (LogReg, SVM, RF)
- Handle class imbalance
- Output probability scores per tag
Priority: P0 (Critical) Story Points: 8
As a data scientist I want to evaluate model performance comprehensively So that we select the best approach for production
Acceptance Criteria:
- Implement Precision@k, Recall@k metrics
- Calculate F1-score for multi-label
- Create confusion analysis for top tags
- Compare all feature extraction approaches
Priority: P0 (Critical) Story Points: 3
As a data scientist I want to optimize model hyperparameters So that we achieve maximum performance
Acceptance Criteria:
- Implement grid/random search
- Use cross-validation
- Track experiments (parameters, scores)
- Document optimal configurations
Priority: P1 (High) Story Points: 5
As a developer I want to expose tag predictions via REST API So that the frontend can request suggestions
Acceptance Criteria:
- POST endpoint for predictions
- Input: question title + body
- Output: list of tags with confidence scores
- Response time < 200ms
Priority: P0 (Critical) Story Points: 5
As a MLOps engineer I want to serialize trained models So that they can be loaded for inference
Acceptance Criteria:
- Save models using joblib/pickle
- Version model artifacts
- Include preprocessing pipeline
- Document model loading procedure
Priority: P0 (Critical) Story Points: 2
As a DevOps engineer I want to deploy the API to cloud infrastructure So that it's accessible for integration
Acceptance Criteria:
- Heroku deployment configuration
- Environment variable management
- Health check endpoint
- Logging and monitoring setup
Priority: P1 (High) Story Points: 3
As a developer I want comprehensive technical documentation So that I can understand and maintain the system
Acceptance Criteria:
- Architecture overview
- API documentation
- Setup instructions
- Code documentation (docstrings)
Priority: P1 (High) Story Points: 5
As a stakeholder I want a user-facing guide So that I understand how to use the system
Acceptance Criteria:
- Feature overview
- Usage examples
- FAQ section
- Troubleshooting guide
Priority: P2 (Medium) Story Points: 3
| Epic | Stories | Total Points | Priority |
|---|---|---|---|
| Data Pipeline | 6 | 13 | P0 |
| Feature Engineering | 4 | 16 | P0-P1 |
| Model Development | 4 | 21 | P0-P2 |
| API & Deployment | 3 | 10 | P0-P1 |
| Documentation | 2 | 8 | P1-P2 |
| Total | 19 | 68 | - |
- US-1.1, US-1.2, US-1.3, US-1.6
- Points: 9
- US-1.4, US-1.5, US-2.1
- Points: 7
- US-2.2, US-2.3, US-2.4
- Points: 13
- US-3.1, US-3.2, US-3.3
- Points: 16
- US-3.4, US-4.1, US-4.2
- Points: 12
- US-4.3, US-5.1, US-5.2
- Points: 11
A user story is considered DONE when:
- Code is written and follows coding standards
- Unit tests pass with >80% coverage
- Code is reviewed
- Documentation is updated
- Feature works in staging environment
- Acceptance criteria are verified
This backlog was executed as part of the IntelliTag freelance mission for Stack Overflow.