Preprocessing
- Split on
;{}and newlines - Merge tiny fragments (< 3 tokens)
- CamelCase / snake_case identifier splitting
- Java keyword + English stopword filtering
NLP · CODE SUMMARIZATION
Four ways to summarize Java source code
Upload a .java file and watch corpus-fitted extractive baselines,
a semantic embedding model, and a fine-tuned transformer each generate a
java code summary, compared live side by side.
4models
liveinference
01 — Architecture
How a file becomes four summaries
Extractive models summarize the whole file from split statements. CodeT5 runs once per Java method, the same setup used in the CodeXGLUE evaluation.
Java upload
A single .java file
Preprocess
Split statements; CodeT5 splits by method
TF
TF-IDF
Term scoring
LR
LexRank
Graph centrality
ST
Sentence-T
Embeddings
T5
CodeT5
Generation
Summary
Four summaries
Corpus fitting
- TF-IDF & LexRank IDF from CodeXGLUE Java train + validation
- Weights cached to
cache/idf_weights_train_val.pkl - Neural models use frozen pretrained checkpoints
- One-time load, then served from memory
Output
- Extractive models return top-N statements from the whole file
- CodeT5 generates one English sentence per method (evaluation setup)
- Per-model latency tracked for each run
- Results compared in a single view
02 — Models
The four summarizers
Each model represents a different tier of prior knowledge. Click a card to expand its step-by-step algorithm, strengths, and limitations.
{% for m in models %}
{% endfor %}
{{ m.tagline }}
{{ m.description }}
How it works
-
{% for step in m.steps %}
- {{ loop.index }}{{ step }} {% endfor %}
Strengths
-
{% for s in m.strengths %}
- {{ s }} {% endfor %}
Limitations
-
{% for l in m.limitations %}
- {{ l }} {% endfor %}
- Input
- {{ m.input }}
03 — Try it
Summarize your Java file
Upload a .java file. Extractive models use the whole file; CodeT5 summarizes each method separately.