Comment-Mine—A Semantic Search Approach to Program Comprehension from Code Comments

Majumdar, Srijoni; Papdeja, Shakti; Das, Partha Pratim; Ghosh, Soumya K.

doi:10.1007/978-981-15-2930-6_3

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…// C1; converting graphics matrix to EPS formats using row major transformations if ( lax ) { //C2; y0 is a 2 dimensional array, converted to vector yp0 = plP_wcpcy(y0); plP_movphy(vppxmi, (PLINT) yp0); plP_draphy(vppxma, (PLINT) yp0); if ( ltx value of ltx should be greater than 20 tp = xtick1 * floor( vpwxmi/xtick1 ); for (;; ){tn= tp +xtick1; { .. }}}} Apart from relating the information from various comments in a source file to source code and metadata, it is necessary to relate them to knowledge assimilated from analyzing runtime traces. We propose SMART-KT, a knowledge assimilation and transfer framework based on multiple sources of a software project in the work, 50,75 and we plan to integrate with CommentProbe to help build up the complete functional requirement of a project from comments.…”

Section: Discussionmentioning

confidence: 99%

“…Apart from relating the information from various comments in a source file to source code and metadata, it is necessary to relate them to knowledge assimilated from analyzing runtime traces. We propose Smart‐KT , a knowledge assimilation and transfer framework based on multiple sources of a software project in the work, 50,75 and we plan to integrate with Comment

P r o b e

to help build up the complete functional requirement of a project from comments.…”

Section: Discussionmentioning

confidence: 99%

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Automated evaluation of comments to aid software maintenance

Majumdar

Bansal

Das

et al. 2022

J Software Evolu Process

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Approaches to evaluate comments based on whether they increase code comprehensibility for software maintenance tasks are important, but largely missing. We propose Comment Probe for automated classification and quality evaluation of code comments of C codebases based on how they can help to understand existing code. We conduct surveys and document developers' perceptions on the type of comments that prove useful to maintaining software in the form of comment categories. A total of 20,206 comments have been collected from open‐source Github projects and annotated with assistance from industry experts. We develop features to semantically analyze comments to locate concepts related to categories of usefulness. Additionally, features based on code and comment correlation are designed to infer whether the comment is also consistent and not superfluous. Using neural networks, comments are classified as useful, partially useful, and not useful with precision and recall scores of 86.27% and 86.42%, respectively. The proposed framework for comment quality evaluation incorporates industry practices and adds significant value to companies wanting to formulate better code commenting strategies. Furthermore, large codebases can be de‐cluttered by removing comments not helpful in maintaining code.

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Section: Discussionmentioning

confidence: 99%

P r o b e

to help build up the complete functional requirement of a project from comments.…”

Section: Discussionmentioning

confidence: 99%

Automated evaluation of comments to aid software maintenance

Majumdar

Bansal

Das

et al. 2022

J Software Evolu Process

Self Cite

View full text Add to dashboard Cite

show abstract

“…Code annotations [14,15] as supporting information for source coding, annotate the details of its code implementation and the purpose of its implementation, which assist developing staff in comprehending, exploring, and managing source code. On the one hand, Abstract Syntax Tree (AST) [16] is a tree-structured data representing source code, which effectively captures the syntactic structure of the source code by reflecting the nesting and dependencies between nodes.…”

Section: Introductionmentioning

confidence: 99%

Smart contract generation model based on code annotation and AST-LSTM tuning

Yong,

Defeng,

Chao

et al. 2024

Preprint

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With the wide application of smart contracts in many fields, the number, types, and complexity of smart contracts are showing a rapidly increasing trend. However , the development of smart contracts has its own unique programming language and security requirements, which are difficult for conventional software personnel to adapt to quickly, and how to realize the efficient development of smart contracts according to the application requirements is an important issue that needs to be solved for its further development. To this end, this paper proposes a smart contract generation method based on AST-LSTM characterization and code annotation tuning large language model, which adopts AST-LSTM model combining Abstract Syntax Tree (AST) and Tree-LSTM to vectorize the code as well as Sentence-Bert to vectorize the annotations and carry out a weighted analysis, and constructs a smart contract clustering analysis model to achieve accurate clustering of functionally similar smart contracts. Then the AST-LSTM+Transformer model is used to detect defects in the clustered code and correlate the related annotation information to construct a diverse Prompt feature prompt statement dataset. Finally, the LLaMA2-7B model is used as the basis for demand-specific smart contract generation with the help of Lora and P-Tuning v2 fine-tuning techniques. In this paper, with the help of BLEU, an auxiliary tool for bilingual translation quality assessment, and Mythril, VaaS, 1 and other code security detection tools, we conducted comparative experiments with existing methods. The results of the experiment show that the average value of BLEU of the code generated by this paper’s method is improved by about 25%, and the code security is improved by about 9%, which will greatly promote the rapid development and exploitation of smart contracts with high-security requirements.

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CSDA: A novel attention-based LSTM approach for code search

Ren

Shan

Wang

et al. 2020

J. Phys.: Conf. Ser.

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Previous studies have proposed semantic-based approaches for code search over large-scale codebases, which has bridged the gap in understanding the semantics between natural language and source-code language. However, these studies either failed to determine an effective method for semantic representations or lacked the distinction of semantic features. In this study, we propose a novel attention-based LSTM neural network known as CSDA (Code Search based on Description Attention), which can effectively improve the code search performance. The proposed model can focus on different parts of a semantic feature when numerous aspects of a source code snippet are used as input. As opposed to assigning the same weight to different parts of the semantic vector, CSDA takes the semantics of natural language descriptions into account, so that the subtle differences hidden in the code snippet can be discriminated and associated with the corresponding queries. We compare CSDA with the existing state-of-the-art approach CODEnn, which uses a jointly embedding technique for code search. Our experimental evaluation demonstrates that CSDA outperforms previous methods and achieves superior code search performance to CODEnn, with higher success rates and mean reciprocal ranks. This study provides significant insights into the use of semantic representation methods in deep learning-based code search approaches.

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Comment-Mine—A Semantic Search Approach to Program Comprehension from Code Comments

Cited by 4 publications

References 18 publications

Automated evaluation of comments to aid software maintenance

Automated evaluation of comments to aid software maintenance

Smart contract generation model based on code annotation and AST-LSTM tuning

CSDA: A novel attention-based LSTM approach for code search

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