DeepFix Program Repair System
A DeepFix Program Repair System is an Automatic Program Repair System that can automatically fixs C Language programming errors.
- Context:
- It was initially developed by Gupta et al. (2017).
- It is based on a Neural Sequence-to-Sequence Network and Recurrent Encoder-Decoder Neural Network models.
- Example(s):
- Counter-Example(s):
- See: DeepFix Seq2Seq Neural Network, Encoder-Decoder Neural Network, Natural Language Processing Task, Sequence Learning Task, Word Sense Disambiguation, LSTM, Deep Neural Network, Memory Augmented Neural Network Training System, Deep Sequence Learning Task, Bidirectional LSTM.
References
2017
- (Gupta et al., 2017) ⇒ Rahul Gupta, Soham Pal, Aditya Kanade, and Shirish Shevade. (2017). “DeepFix: Fixing Common C Language Errors by Deep Learning.” In: Proceeding of AAAI.
- QUOTE: We present an end-to-end solution, called DeepFix, that does not use any external tool to localize or fix errors. We use a compiler only to validate the fixes suggested by DeepFix. At the heart of DeepFix is a multi-layered sequence-to-sequence neural network with attention (Bahdanau, Cho, and Bengio 2014), comprising of an encoder recurrent neural network (RNN) to process the input and a decoder RNN with attention that generates the output. The network is trained to predict an erroneous program location along with the correct statement. DeepFix invokes it iteratively to fix multiple errors in the program one-by-one.