add 资讯科技研究方法 proposal pre

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examples/markdown.md

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-# RUG: Turbo LLM for Rust Unit Test Generation
+# JSONite: High-Performance Embedded Database for Semi-Structured Data
 
-Keywords: LLM, Rust, Unit Test
+---
 
-Research date: 2022, published date: 2025
+## The JSON Performance Crisis
+
+**JSON is Everywhere:**
+- Web APIS, IoT, logs, configurations
+- Semi-structured, flexible, human-readable
+
+**But Current Solutions Fail:**
+- **Large Databases**: People use MongoDB or PostgreSQL's JSONB to store data
+- **Embeded Databases**: RocksDB and PoloDB lack of ACID and SQL support
+- **Serialization to String**: Or serialize JSON into strings and store in SQLite
 
 
+Serialized JSON with SQL example
 
-#### Introduction
+```sql
+insert into http_request_log (ip, headers)
+values ('127.0.0.1', '{
+    "Content-Type": "application/oct-stream",
+    "X-Forwarded-For": "100.64.0.1",
+}');
+```
 
-*   Unit testing is crucial but costly.
+---
 
-*   Rust's strict type system.
+## Introducing JSONite
 
-*   Existing LLM approaches often fail.
+**Best of Both Worlds:**
+- SQLite's based
+- Native JSON optimization
 
+**Key Advantages:**
+- ✅ ACID compliance
+- ✅ SQL simplicity  
+- ✅ Serverless C library
+- ✅ Lightning-fast JSON access
 
-#### Rust Unit Test
+---
 
-```rust
+## Smart Key Optimization
-/// Returns the sum of two numbers
+
-///
+**Key Sorting by Length:**
-/// # Examples
+```
-///
+{
-/// ```
+  "id": 1,
-/// assert_eq!(add(2, 3), 5);
+  "address": {...}
-/// assert_eq!(add(-1, 1), 0);
+  "name": "John",
-/// ```
+  "email": "john@example.com",
-fn add(a: i32, b: i32) -> i32 {
-    a + b
 }
 ```
 
-
+**Sorted as:**
-#### Challenge
+```
-
+{
-```rust
+  "id", (2 chars)
-fn encode<E: Encoder>(&self: char, encoder: E) -> Result<EncodeError> // target function
+  "name", (4 chars)  
-
+  "email", (5 chars)
-impl<W: Writer, C: Config> Encoder for EncoderImpl
+  "address", (7 chars)
-
+}
-pub struct EncoderImpl<W: Writer, C: Config>
-impl Writer for SliceWriter
-impl Writer for IoWriter
-
-impl<T> Config for T where T: R1 + R2 + R3
-pub struct Configuration<R1, R2, R3>
 ```
 
-Simplified python version
+**Binary search on length → Fast lookups**
 
-```python
+---
-def encode(char_data, encoder):
-    result = encoder.process(char_data)
-    return result
 
-class Encoder:
+## Handling Massive Data: Smart TOAST
-    def __init__(self, writer, config):
-        self.config = config
 
-    def process(self, data):
+**The Oversized-Attribute Storage Technique**
-        output = self.writer.write(data, self.config)
+- Standard approach: arbitrary chunking
-        return output
+- JSONite's innovation: **Data-Type Aware TOAST**
 
-class Config:
+**Intelligent Chunking:**
-    def __init__(self):
+- Arrays split between elements
-        self.settings = {}
+- Objects split between key-value pairs  
+- Text falls back to fixed chunks
 
-config = Config()
+**Enables "Slice Detoasting":**
-encoder = Encoder(stdout, config)
+- `$.logs[1000000:1000010]` fetches only 10 elements
+- Not the entire multi-gigabyte array
 
-# Test code
+
-result = encode('A', encoder)
+Smart Chunking Example
+
+```json
+{
+    "id": 1,
+    "title": "some text",
+    "html": <pointer to TOAST of 200k text>,
+    "photos": [<pointer to TOAST of binary data>],
+    "crawl_logs": [<pointer to TOAST of array of texts>]
+}
 ```
 
-LLM generated code are hard to pass the compiler.
+---
 
+## Query Power
 
+**Full SQL + JSON Support:**
+- PostgreSQL-compatible JSONB path operators
+- GIN indexes for instant search
 
-#### RUG design
+```sql
+SELECT *
+FROM accounts
+WHERE data @> '{"status": "active"}'
+```
 
-<img src="./images/Screenshot_20251125_010053.jpeg"
+---
-  width="75%">
 
+## Performance Validation: Benchmark Datasets
 
+**Three Specialized Workloads:**
 
-<img src="./images/Screenshot_20251125_011029.jpeg" width="80%">
+1. **YCSB-Style Read Benchmark**
+   - Yahoo! Cloud Serving Benchmark
+   - 1M JSON documents (1KB-100KB each)
 
-<img src="./images/Screenshot_20251125_011348.jpeg" width="80%">
+2. **TPC-C Inspired Update Benchmark**  
+   - Transaction Processing Performance Council
+   - 100K transactional JSON records
+   - Frequent small field updates
 
+3. **Large-Array Slice Benchmark**
+   - Multi-gigabyte JSON documents
+   - Massive arrays (10M+ elements)
 
+**Comparison Targets:** SQLite JSONB vs MongoDB vs PostgreSQL vs JSONite
 
-#### Implementation
+---
 
-- gpt-3.5-turbo-16k-0613
+## JSONite: The Future of Embedded Data Storage
-- gpt-4-1106
-- presence penalty set to -1
-- frequency_penalty set to 0.5
-- temperature set to 1 (by default)
 
+**Why It Matters Today:**
+- **Edge Computing**: Lightweight, handles sensor data efficiently
+- **Modern Apps**: SQL power + JSON flexibility, no schema migrations
 
+**The Vision:**
+- Open source implementation
+- Community-driven development  
+- Becoming the default choice for embedded JSON storage
+- Bridging SQL reliability with NoSQL flexibility
 
-#### Eval: Comparison with Traditional Tools
+---
 
-<img src="./images/Screenshot_20251125_014355.jpeg" width="75%">
+## Thank You
 
+**Questions?**
 
-#### Token Consumption
+*CHEN Yongyuan*  
-
+*2025-11-01*
-- GPT-4 cost 1000$ in baseline method (send the whole context)
-- RUG saved 51.3% tokens (process unique dependency only once)
-
-
-
-#### Real-World Usability
-
-> We directly leverage RUG's generated tests, without changing test bodies and send them as PRs to the open source projects.
-> To our surprise, the developers are happy to merge these machine generated tests.
-> RUG generated a total of 248 unit tests, of which we submitted 113 to the corresponding crates based on their quality and priority.
-> So far, 53 of these unit tests have been merged with positive feedback.
-
-> Developers chose not to merge 17 tests for two main reasons:
-> first, the target functions are imported from external libraries(16),
-> and the developers do not intend to include tests
-
-
-
-#### 2025 Situation
-
-<img src="./images/Screenshot_20251125_015416.jpeg" width="60%">
-
-
-<img src="./images/Screenshot_20251125_015705.jpeg" width="60%">