SLICEFORMER: Static Program Slicing Using Language Models With Dataflow-Aware Pretraining and Constrained Decoding (2026.acl-long)
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| Challenge: | Static program slicing is a software engineering technique for isolating code relevant to specific variables. |
| Approach: | They propose a new approach that reformulates static program slicing as a sequence-to-sequence task using small language models such as CodeT5+. |
| Outcome: | The proposed approach improves on Java and Python program slicing benchmarks with up to 22% gain in ExactMatch. |
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UniXcoder: Unified Cross-Modal Pre-training for Code Representation (2022.acl-long)
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| Challenge: | Pre-trained models for programming languages have demonstrated great success on code intelligence . however, such pre-tried models are sub-optimal for auto-regressive tasks . |
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CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation (2021.emnlp-main)
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| Challenge: | Pre-trained models for Natural Languages (NL) like BERT and GPT have been shown to transfer well to Programming Languages. |
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| Challenge: | Existing models for code generation from natural language do not allow reconsidering earlier tokens . prior work has explored grouped beam search or nucleus sampling to generate diverse text. |
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| Challenge: | PLUG is a programming language that is used for programming and language understanding and generation tasks. |
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| Challenge: | Large pre-trained language models for textual data have an unconstrained output space . when fine-tuned to target constrained formal languages like SQL, these models often generate invalid code, rendering it unusable. |
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UER: An Open-Source Toolkit for Pre-training Models (D19-3)
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Zhe Zhao, Hui Chen, Jinbin Zhang, Xin Zhao, Tao Liu, Wei Lu, Xi Chen, Haotang Deng, Qi Ju, Xiaoyong Du
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| Challenge: | Existing pretraining models take the first 256 tokens of code snippets by default, limiting the input length to 512. |
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Code Representation Pre-training with Complements from Program Executions (2024.emnlp-industry)
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| Challenge: | Existing languages have syntactic representations of code to improve code intelligence, but they are difficult to learn from code. |
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Exploring Data Augmentation for Code Generation Tasks (2023.findings-eacl)
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| Challenge: | Recent advances in natural language processing have impacted how models are trained for programming language tasks. |
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