Update README.md
Browse files
README.md
CHANGED
@@ -92,4 +92,66 @@ prompt = f"Generate unit tests in Dart for the following class:\n{input_code}"
|
|
92 |
# Generate tests
|
93 |
inputs = tokenizer(prompt, return_tensors="pt")
|
94 |
outputs = model.generate(**inputs, max_length=512)
|
95 |
-
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
92 |
# Generate tests
|
93 |
inputs = tokenizer(prompt, return_tensors="pt")
|
94 |
outputs = model.generate(**inputs, max_length=512)
|
95 |
+
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
|
96 |
+
|
97 |
+
## Training Details
|
98 |
+
|
99 |
+
### Training Data
|
100 |
+
|
101 |
+
The fine-tuning dataset consists of **16,252 Dart code-test pairs** extracted from open-source GitHub repositories using Google BigQuery. The data was subjected to quality filtering and deduplication to ensure high relevance and consistency.
|
102 |
+
|
103 |
+
### Training Procedure
|
104 |
+
|
105 |
+
- **Fine-tuning Approach:** Supervised Fine-Tuning (SFT) with QLoRA for memory efficiency.
|
106 |
+
- **Hardware:** Training was conducted on a single NVIDIA A100 GPU.
|
107 |
+
- **Optimization:** Flash Attention 2 was utilized for enhanced performance.
|
108 |
+
- **Duration:** The training process ran for up to 32 hours.
|
109 |
+
|
110 |
+
### Training Hyperparameters
|
111 |
+
|
112 |
+
- **Mixed Precision:** FP16
|
113 |
+
- **Optimizer:** AdamW
|
114 |
+
- **Learning Rate:** 5e-5
|
115 |
+
- **Epochs:** 3
|
116 |
+
|
117 |
+
### Environmental Impact
|
118 |
+
|
119 |
+
- **Hardware Type:** NVIDIA A100 GPU
|
120 |
+
- **Hours Used:** 32 hours
|
121 |
+
- **Carbon Emitted:** 13.099 kgCO2eq
|
122 |
+
|
123 |
+
---
|
124 |
+
|
125 |
+
## Evaluation
|
126 |
+
|
127 |
+
### Testing Data, Factors & Metrics
|
128 |
+
|
129 |
+
- **Testing Data:** A subset of **42 Dart files** from the training dataset, evaluated in a zero-shot setting.
|
130 |
+
- **Factors:** Syntax correctness, functional correctness.
|
131 |
+
- **Metrics:** pass@1, syntax error rate, functional correctness rate.
|
132 |
+
|
133 |
+
### Results
|
134 |
+
|
135 |
+
- **Syntax Correctness:** +76% improvement compared to the base model.
|
136 |
+
- **Functional Correctness:** +16.67% improvement compared to the base model.
|
137 |
+
|
138 |
+
---
|
139 |
+
|
140 |
+
## Citation
|
141 |
+
|
142 |
+
If you use this model in your research, please cite:
|
143 |
+
|
144 |
+
**BibTeX:**
|
145 |
+
```bibtex
|
146 |
+
@inproceedings{hoffmann2024testgen,
|
147 |
+
title={Test Case Generation with Fine-Tuned LLaMA Models},
|
148 |
+
author={Hoffmann, Jacob and Frister, Demian},
|
149 |
+
booktitle={Proceedings of the 29th ACM/SIGSOFT International Workshop on Automated Software Testing (AST)},
|
150 |
+
year={2024},
|
151 |
+
doi={10.1145/3644032.3644454}
|
152 |
+
}
|
153 |
+
|
154 |
+
## Model Card Contact
|
155 |
+
|
156 |
+
- **Jacob Hoffmann**: [[email protected]](mailto:[email protected])
|
157 |
+
- **Demian Frister**: [[email protected]](mailto:[email protected])
|