1429 lines
87 KiB
Clojure
1429 lines
87 KiB
Clojure
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:content {:text " Neural Machine Translation (NMT) "},
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:content {:text "6.2 Limitations Our approach mainly focuses on the single line source code editing. However, when using the binary cross-entropy loss to replace the current standard cross-entropy loss, the localization parts of our approach can also handle multi-line source code editing by setting a threshold to filter multiple results as the predicted lines"},
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:content {:text "In this paper, we investigate the performance of existing standard sequence-to-sequence multimodal learning for code editing under a more practical situation, in which the precise line-level location information is unknown or unavailable. Through comprehensive experiments, we have confirmed that the models are not able to generate precise edits after training without exact line-level location information. To tackle this challenge, we proposed jLED (jointly Localize and EDit), a training pipeline to jointly learn to localize the edit buggy code simultaneously, which enables models to learn additional knowledge and abilities regarding to the line-level localization while learning to edit. We conduct experiments using our proposed jLED, and the experimental results show that our approach not only generates more precise edits, but also predicts more accurate line-level locations than the considered baselines. Moreover, to evaluate the effectiveness of our joint learning pipeline against other localization and editing alternatives, we construct a two-stage localization-editing pipeline, in which a localization model and a editing model are trained separately. Experimental results demonstrate the superiority of our jLED over this two-stage manner"},
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:content {:text " https://anonymous.4open. science/r/Code_Edit_Joint_Learning-1F8D. The remainder of this paper is presented as follows. Section 2 introduces the background of this work. In Section 3, we present our methodology with detailed explanations. Section 4 and 5 cover the experimental design and results. We provide discussions and related work in Section 6 and 7. Section 8 concludes this work.2 BACKGROUND2.1 Neural Machine Translation Neural Machine Translation (NMT) has emerged as a promising approach in the field of machine translation, exhibiting well performance in automating language translation tasks [6 ]. By leveraging deep neural networks, NMT models are capable of learning and generating translations in an end-to-end manner, thereby overcoming the limitations of traditional statistical machine translation methods. At its core, NMT comprises two fundamental components: the encoder and the decoder, which work synergistically to facilitate the translation process. The encoder component plays a crucial role in comprehending and processing the input sentence, utilizing sophisticated neural architectures to generate a vector representation that encapsulates the underlying semantic meaning of the source text. This vector representation serves as a rich and comprehensive representation of the source sentence, enabling the subsequent translation process to leverage the encoded information effectively. The decoder component, on the other hand, capitalizes on the encoded input representation to sequentially generate the target sentence through a process of logical reasoning. In recent years, the field of Software Engineering (SE) has witnessed a broad range of applications for NMT. Notably, NMT has found utility in areas such as Automatic Program Repair [20, 30], Program Synthesis [65], and Code Edit Generation [55, 56]. These applications capitalize on NMT’s ability to comprehend and generate intricate patterns, making it a valuable tool for SE-related tasks. The integration of NMT in software engineering research highlights its potential to enhance various aspects of software development, offering new avenues for improving program understanding, code generation, and automated repair.2.2 Transformer Model for Sequence Processing Transformer model [ 58] has emerged as a highly influential and prominent model for sequence processing tasks within the field of natural language processing (NLP), leading to numerous state-of-the-art achievements [3 , 61]. Prior to the advent of the Transformer, recurrent neural networks (RNNs) and their variants, such as long short-term memory (LSTM) and gated recurrent units (GRUs), were conventionally utilized for sequence processing tasks. While RNNs showcased commendable performance, they encountered challenges in parallelization and capturing longrange dependencies adequately. To overcome these limitations, the Transformer model introduced a novel self-attention mechanism, enabling selective attention to different parts of the input sequence through adaptive weighting. This mechanism played a pivotal role in capturing dependencies between distinct positions within the sequence, facilitating parallel processing of the input. During the token representation learning phase, the Transformer model learned to attend to all input tokens, transforming the sequence into a comprehensive graph where each token represented a node. The edge weights in this graph denoted the attention weights between tokens, which were learned based on the specific task at hand. Additionally, positional encoding was incorporated J. ACM, Vol. 1, No. 1, Article . Publication date: October 2023. Page 4 of 22Transactions on Software Engineering and Methodology1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556"},
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