Updated: 2025-12-28 Sun 19:48

# iseweek1

planted: 2025-03-29 last tended to: 2025-12-28

## Bug report classification

Perform bug report classification with AI following the steps below:

- Text filtering
- Stemming
- Indexing
- Train machine learning models, i.e., Decision Tree, Naive Bayes, and Logistic Regression
- Prediction (after data pre-processing)

### Text filtering:

- Punctuation removal
- Specific transformations occur: the myUser has no meaning, its just a word. You need to split it into two meaningful words.
- Stop words should not be removed, as it changes the whole meaning of a bug report.
- Unlike other NLP tasks, removing stop words may distort the meaning of bug reports.

### Stemming:

- Stemming reduces different forms of a word to its common base by sequential application of stemming rules. For example: light caresses colours becomes Light caress colour

### Indexing:

- Indexing: we need to convert the text into numeric representation, there are two common ways of doing this: TF and IDF
- TF: total words / specific word . for example: “bug” appears 50 times in a 150 terms.
- IDF: We penalise certain words based on their frequency.
- bug report classifications here often does not use IDF but TF, because some frequent terms should not be penalized
- For each report, we will have a vector of indexing for words in the reports according to a dictionary
- Example: A bug report = {“this”, “bugs”, “failure”, “interesting”, …} Vector = <0.123, 0.34, 0.1, ….>

### DT

- What is decision tree: a tree structure that split the data depending on different values.
- Each node is a test on the attribute.
- Each branch represents the outcome.

### NB

- What is Naive Bayes? Classifier based on the conditional probability given by the Bayes theorem.
- Calculating the probability of each class, and classify the given features into the one with higher probability.

### LR

- What is logistic regression: a linear model for classification
- Fitting the logistic growth and the sigmoid midpoint
- Output a probability, but can use a cutoff point to decide class.

### Results:

Choose the top 20/50 words when theyre used as a feature

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