Lancer: Your Code Tell Me What You Need

Zhou, Shufan; Shen, Beijun; Zhong, Hao

doi:10.1109/ase.2019.00137

Cited by 28 publications

(10 citation statements)

References 11 publications

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“…BERT is a deep representation learning model for languages and has been widely used in the domain of software engineering. For example, Zhou et al [42] applied the BERT model to extract semantic features from code identifiers of programs to perform code recommendation. Yu et al [43] leveraged BERT on binary code to identify similar binaries.…”

Section: Methodsmentioning

confidence: 99%

SmartGift: Learning to Generate Practical Inputs for Testing Smart Contracts

Zhou

Liu

et al. 2021

2021 IEEE International Conference on Software Maintenance and Evolution (ICSME)

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Section: Methodsmentioning

confidence: 99%

SmartGift: Learning to Generate Practical Inputs for Testing Smart Contracts

Zhou

Liu

et al. 2021

2021 IEEE International Conference on Software Maintenance and Evolution (ICSME)

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“…They improve the query quality by leveraging the knowledge of token frequency in the codebase, and finally re-rank the searched candidate code based on the TF-IDF weighting method. Zhou et al (2019) proposed Lancer, a context-aware code to-code recommending tool. Lancer uses a Library-Sensitive Language Model and a BERT model to recommend relevant code samples in real-time based on the incomplete code.…”

Section: Related Workmentioning

confidence: 99%

Clone-Seeker: Effective Code Clone Search Using Annotations

et al. 2022

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Source code search plays an important role in software development, e.g. for exploratory development or opportunistic reuse of existing code from a code base. Often, exploration of different implementations with the same functionality is needed for tasks like automated software transplantation, software diversification, and software repair. Code clones, which are syntactically or semantically similar code fragments, are perfect candidates for such tasks. Searching for code clones involves a given search query to retrieve the relevant code fragments. We propose a novel approach called Clone-Seeker that focuses on utilizing clone class features in retrieving code clones. For this purpose, we generate metadata for each code clone in the form of a natural language document. The metadata includes a pre-processed list of identifiers from the code clones augmented with a list of keywords indicating the semantics of the code clone. This keyword list can be extracted from a manually annotated general description of the clone class, or automatically generated from the source code of the entire clone class. This approach helps developers to perform code clone search based on a search query written either as source code terms, or as natural language. With various experiments, we show that (1) Clone-Seeker is effective in finding clones from BigCloneBench dataset by applying code queries and natural language queries; 2) Clone-Seeker has a higher recall when searching for semantic code clones (i.e., Type-4) in BigCloneBench than the state-of-the-art; 3) Clone-Seeker is a generalized technique as it is effective in finding clones in Project CodeNet dataset by applying code queries and natural language queries. 4) Clone-Seeker with manual annotation outperforms other variants in finding clones on the basis of natural language queries.

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“…However, the purpose of our fine-tuned GPT-2 model is to predict next tokens/clone methods based on user input. Lancer ( Zhou, Shen & Zhong, 2019 ) is a context-aware code-to-code recommendation tool leveraging a Library-Sensitive Language Model and a BERT model to recommend relevant code samples in real-time, by automatically analyzing the intention of the incomplete code. Lancer uses the BERT model to complete an incomplete code sample.…”

Section: Related Workmentioning

confidence: 99%

Clone-advisor: recommending code tokens and clone methods with deep learning and information retrieval

Hammad

Babur

Basit

et al. 2021

PeerJ Computer Science

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Software developers frequently reuse source code from repositories as it saves development time and effort. Code clones (similar code fragments) accumulated in these repositories represent often repeated functionalities and are candidates for reuse in an exploratory or rapid development. To facilitate code clone reuse, we previously presented DeepClone, a novel deep learning approach for modeling code clones along with non-cloned code to predict the next set of tokens (possibly a complete clone method body) based on the code written so far. The probabilistic nature of language modeling, however, can lead to code output with minor syntax or logic errors. To resolve this, we propose a novel approach called Clone-Advisor. We apply an information retrieval technique on top of DeepClone output to recommend real clone methods closely matching the predicted clone method, thus improving the original output by DeepClone. In this paper we have discussed and refined our previous work on DeepClone in much more detail. Moreover, we have quantitatively evaluated the performance and effectiveness of Clone-Advisor in clone method recommendation.

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Lancer: Your Code Tell Me What You Need

Cited by 28 publications

References 11 publications

SmartGift: Learning to Generate Practical Inputs for Testing Smart Contracts

SmartGift: Learning to Generate Practical Inputs for Testing Smart Contracts

Clone-Seeker: Effective Code Clone Search Using Annotations

Clone-advisor: recommending code tokens and clone methods with deep learning and information retrieval

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