On the Use of Automated Text Summarization Techniques for Summarizing Source Code

Haiduc, Sonia; Aponte, Jairo; Moreno, Laura; Marcus, Andrian

doi:10.1109/wcre.2010.13

Cited by 327 publications

(285 citation statements)

References 24 publications

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“…It is performed by concatenating several sentences taken exactly as they appear in the input being summarized. Summary's length depends on the compression rate [5].…”

Section: Extractive Summaries (Extracts)mentioning

confidence: 99%

“…Text summarization is a difficult task which preferably involves deep natural language processing capacities [5] and in order to simplify the issue current research is focused on extractive summary generation. Summary can be generated through either extractive or Abstractive summarization technique.…”

Section: Introductionmentioning

confidence: 99%

“…Large amount of annotated or labeled data is needed for learning techniques. These systems are addressed at sentence level as two-class classification problem in which sentences belonging to the summary are termed as positive samples and sentences not present in the summary are named as negative samples [5]. Some of the classification methods used in machine learning is Support Vector Machine (SVM) [5] and neural networks [5].Unsupervised systems do not need any training data.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automatic Text Summarization

Chettri¹,

Chakraborty²

2017

IJCA

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Summarization is the art of abstracting key content from one or more information sources [6]. Summarization includes text summarization, image summarization, and video summarization. Text summarization is one of application of natural language processing and is becoming more popular for information condensation [1].Information is accessible in great quantity for every topic on internet assembly the key information in the form of summary would benefit a number of users. Automatic text summarization system generates a summary, i.e. it contains short length text which comprises all the key information of the document. Summary can be generated through extractive as well as abstractive methods.

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“…It is performed by concatenating several sentences taken exactly as they appear in the input being summarized. Summary's length depends on the compression rate [5].…”

Section: Extractive Summaries (Extracts)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Text Summarization

Chettri¹,

Chakraborty²

2017

IJCA

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“…Haiduc et al [6] mentioned that a combination of automated text summarization techniques is more reliable for source code and helps in better program comprehension. They focused on investigating the suitability of several summarization techniques, mostly based on text retrieval methods, to capture source code semantics in a way similar to how developers understand it.…”

Section: Related Workmentioning

confidence: 99%

Summarizing Services of Java Packages

Hammad¹,

Abuljadayel²,

Khalaf³

2016

LNSE

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Abstract-Program comprehension is essential for code maintenance and evolution activities. It saves time and efforts of developers who want to perform any code changes. It also minimizes the chances of introducing bugs. Textual summaries for source code provide great help to code understanding activities. This paper presents an approach to automatically generate textual summaries for services implemented in java packages. The summary is generated by analyzing the source code of methods defined the package. Each method represents a service provide by the package. Each service is summarized as a natural language textual description. The generated summary for a method mainly includes the used data and the names of invoked methods. Summaries of all methods defined in a package are refined and integrated to be reported as a comprehensive summary for the services provided by the package. The generated summaries are useful in different ways. They can be used by developers in their maintenance activities. They also can be useful for the documentation purposes. IndexTerms-Program comprehension, software maintenance, source code summarization.

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“…They have proposed to use natural processing techniques, and statistical machine translation approach for tasks like code summarization. Haiduc et al (2010) have worked on summarizing the source code by using Latent Semantic Indexing and Vector Space Model to generate summaries for a java code. Recent works as Nguyen et al (2013), and Movshovitz-Attias and Cohen (2013) have used n-grams language models to show the predictability of the software, and using that to generate the project documentation for the source code.…”

Section: Related Workmentioning

confidence: 99%

Using machine translation for converting Python 2 to Python 3 code

Aggarwal

Salameh

Hindle

2015

Preprint

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In this paper, we have tried to use Statistical machine translation in order to convert Python 2 code to Python 3 code. We use data from two projects and achieve a high BLEU score. We also investigate the cross-project training and testing to analyze the errors so as to ascertain differences with previous case. We have described a pilot study on modeling programming languages as natural language to build translation models on the lines of natural languages. This can be further worked on to translate between versions of a programming language or crossprogramming-languages code translation.

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On the Use of Automated Text Summarization Techniques for Summarizing Source Code

Cited by 327 publications

References 24 publications

Automatic Text Summarization

Automatic Text Summarization

Summarizing Services of Java Packages

Using machine translation for converting Python 2 to Python 3 code

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