Autofolding for Source Code Summarization

Fowkes, Jaroslav; Chanthirasegaran, Pankajan; Ranca, Razvan; Allamanis, Miltiadis; Lapata, Mirella; Sutton, Charles

doi:10.1109/tse.2017.2664836

Cited by 44 publications

(17 citation statements)

References 56 publications

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“…The supervised learning method is the classification, and the unsupervised learning method is clustering. In the first approach is to prepare data with labels are predefined and assign to a new record [40]. Unsupervised learning allocates a set of every record in a data collection based on clustering similarity functions.…”

Section: Health and Medical Topic Modelingmentioning

confidence: 99%

Machine learning analysis of topic modeling re-ranking of clinical records

Kakulapati¹,

Reddy²,

Deepthi

et al. 2020

Smart Biosensors in Medical Care

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Technologies in Big data have improved the analysis of clinical information for better understanding diseases in order to provide more efficient diagnoses. An online healthcare system has created huge data by record maintaining, taking into account acceptable requirements and the patient's care. These clinical records are in files that pose a challenge for data processing and finding relevant documents. In this work, we used a method that combines Statistical Topic Models, Language Models and Natural Language Processing, in order to retrieve clinical records.On the other hand, for analysing large clinical records in the form of documents, Topic models are used to finding related clusters of disease patterns. Here, it is explored the decomposition of clinical record summaries into topics which enables the effective clustering of relevant documents based on the topic under study. Clinical documents selected in a Topic-based approach give proper information to the users for better understanding and derive insights from the related data. In our proposed method, it is used clustering-based semantic similarity topic modelling in order to summarizing the clinical reports based on Latent Dirichlet Allocation (LDA) in a MapReduce framework. Automated unsupervised analysis of LDA models are used to identify different disease patterns and to rank topic significance. In this, topic and keyword re-ranking methods which assist physicians to get improved information through the LDA-obtained topics. The experimental assessment confirmed the value of the used methods in clinical documents summarization.

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Section: Health and Medical Topic Modelingmentioning

confidence: 99%

Machine learning analysis of topic modeling re-ranking of clinical records

Kakulapati¹,

Reddy²,

Deepthi

et al. 2020

Smart Biosensors in Medical Care

View full text Add to dashboard Cite

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“…Fowkes et al [48] Source Code Intrinsic (Precision, Accuracy, Recall, FMeasure) + Extrinsic Usefulness, Conciseness evaluation. Rastkar et al [13] used extrinsic task based evaluation to evaluate if the summaries so generated helps perform duplicate bug reports detection task.…”

Section: Extrinsic Evaluationmentioning

confidence: 99%

“…TASSAL [48] is one of the example of content based model for creating source code summaries but deep learning and other techniques can be used for content based model to create good coverage summaries which to capture the class relationships and other semantic information of source code. Fowkes et al [48] autofolded the source code at file level, autofolding at statement level can be considered for future work. Automatic documents generation tools which produce documents by summarizing source code are not very expressive [41], work is required to produce more readable and expressive messages.…”

Section: Source Code Summarizationmentioning

confidence: 99%

Summarization of Software Artifacts : A Review

Gupta¹,

S.K²

2017

IJCSIT

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Summarization of software artifacts is an ongoing field of research among the software engineering community due to the benefits that summarization provides like saving of time and efforts in various software engineering tasks like code search, duplicate bug reports detection, traceability link recovery, etc. Summarization is to produce short and concise summaries. The paper presents the review of the state of the art of summarization techniques in software engineering context. The paper gives a brief overview to the software artifacts which are mostly used for summarization or have benefits from summarization. The paper briefly describes the general process of summarization. The paper reviews the papers published from 2010 to June 2017 and classifies the works into extractive and abstractive summarization. The paper also reviews the evaluation techniques used for summarizing software artifacts. The paper discusses the open problems and challenges in this field of research. The paper also discusses the future scopes in this area for new researchers.

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“…Features related to dialogue acts are discovered and utilized for meeting summarization. An unsupervised method for the automatic summarization of source code text is proposed by Fowkes et al [40]. The proposed technique is utilized for code folding, which allows one to selectively hide blocks of code.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Summarizing large text collection using topic modeling and clustering based on MapReduce framework

Nagwani

2015

Journal of Big Data

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Document summarization provides an instrument for faster understanding the collection of text documents and has a number of real life applications. Semantic similarity and clustering can be utilized efficiently for generating effective summary of large text collections. Summarizing large volume of text is a challenging and time consuming problem particularly while considering the semantic similarity computation in summarization process. Summarization of text collection involves intensive text processing and computations to generate the summary. MapReduce is proven state of art technology for handling Big Data. In this paper, a novel framework based on MapReduce technology is proposed for summarizing large text collection. The proposed technique is designed using semantic similarity based clustering and topic modeling using Latent Dirichlet Allocation (LDA) for summarizing the large text collection over MapReduce framework. The summarization task is performed in four stages and provides a modular implementation of multiple documents summarization. The presented technique is evaluated in terms of scalability and various text summarization parameters namely, compression ratio, retention ratio, ROUGE and Pyramid score are also measured. The advantages of MapReduce framework are clearly visible from the experiments and it is also demonstrated that MapReduce provides a faster implementation of summarizing large text collections and is a powerful tool in Big Text Data analysis.

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Autofolding for Source Code Summarization

Cited by 44 publications

References 56 publications

Machine learning analysis of topic modeling re-ranking of clinical records

Machine learning analysis of topic modeling re-ranking of clinical records

Summarization of Software Artifacts : A Review

Summarizing large text collection using topic modeling and clustering based on MapReduce framework

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