TCProF: Time-Complexity Prediction SSL Framework
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908276551057408 |
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| author | Hahn, Joonghyuk Ahn, Hyeseon Kim, Jungin Lim, Soohan Han, Yo-Sub |
| author_facet | Hahn, Joonghyuk Ahn, Hyeseon Kim, Jungin Lim, Soohan Han, Yo-Sub |
| contents | Time complexity is a theoretic measure to determine the amount of time the algorithm needs for its execution. In reality, developers write algorithms into code snippets within limited resources, making the calculation of a code's time complexity a fundamental task. However, determining the precise time complexity of a code is theoretically undecidable. In response, recent advancements have leaned toward deploying datasets for code time complexity prediction and initiating preliminary experiments for this challenge. We investigate the challenge in low-resource scenarios where only a few labeled instances are given for training. Remarkably, we are the first to introduce TCProF: a Time-Complexity Prediction SSL Framework as an effective solution for code time complexity prediction in low-resource settings. TCProF significantly boosts performance by integrating our augmentation, symbolic modules, and a co-training mechanism, achieving a more than 60% improvement over self-training approaches. We further provide an extensive comparative analysis between TCProF, ChatGPT, and Gemini-Pro, offering a detailed evaluation of our approach. Our code is at https://github.com/peer0/few-shot-tc. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_15749 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TCProF: Time-Complexity Prediction SSL Framework Hahn, Joonghyuk Ahn, Hyeseon Kim, Jungin Lim, Soohan Han, Yo-Sub Software Engineering Artificial Intelligence 68T50 I.2.7 Time complexity is a theoretic measure to determine the amount of time the algorithm needs for its execution. In reality, developers write algorithms into code snippets within limited resources, making the calculation of a code's time complexity a fundamental task. However, determining the precise time complexity of a code is theoretically undecidable. In response, recent advancements have leaned toward deploying datasets for code time complexity prediction and initiating preliminary experiments for this challenge. We investigate the challenge in low-resource scenarios where only a few labeled instances are given for training. Remarkably, we are the first to introduce TCProF: a Time-Complexity Prediction SSL Framework as an effective solution for code time complexity prediction in low-resource settings. TCProF significantly boosts performance by integrating our augmentation, symbolic modules, and a co-training mechanism, achieving a more than 60% improvement over self-training approaches. We further provide an extensive comparative analysis between TCProF, ChatGPT, and Gemini-Pro, offering a detailed evaluation of our approach. Our code is at https://github.com/peer0/few-shot-tc. |
| title | TCProF: Time-Complexity Prediction SSL Framework |
| topic | Software Engineering Artificial Intelligence 68T50 I.2.7 |
| url | https://arxiv.org/abs/2502.15749 |