2015 IEEE International Conference on Software Maintenance and Evolution (ICSME) 2015
DOI: 10.1109/icsm.2015.7332514
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Using stereotypes in the automatic generation of natural language summaries for C++ methods

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Cited by 33 publications
(19 citation statements)
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“…Abid et al [38] used method stereotypes to find out the lines of code that reflect the main action of a method and used the separate templates for each stereotype to automatically create the summaries using static program analysis.…”
Section: Abstractive Summarization: An Overview In the Context Of Thementioning
confidence: 99%
See 2 more Smart Citations
“…Abid et al [38] used method stereotypes to find out the lines of code that reflect the main action of a method and used the separate templates for each stereotype to automatically create the summaries using static program analysis.…”
Section: Abstractive Summarization: An Overview In the Context Of Thementioning
confidence: 99%
“…Most of the focus of studies in source code summarization is for C++ and Java language. Studies can be extended for generating the summaries for other object-oriented languages as well [38]. More work on assessing the quality and effectiveness of summaries for maintenance tasks like feature location or debugging is required [24].…”
Section: Source Code Summarizationmentioning
confidence: 99%
See 1 more Smart Citation
“…We found strong evidence that developers tend to write their summaries from source-code lines that they read the most (longest gaze time). Therefore, we conclude that gaze time can substantially predict lines that are important for summarizing a method [6,7,11,12,17]. An overarching goal of this paper is to investigate ways to improve existing summarization approaches via findings from the eye-tracking study presented.…”
Section: Introductionmentioning
confidence: 96%
“…One way to overcome this problem is to automatically generate summaries directly from source code. Several approaches are proposed to generate automatic summaries using Natural Language Processing (NLP) [6,7], text retrieval [8,9], and static analysis [10,11]. In order to further improve source code summarization techniques [6][7][8][11][12][13][14][15][16], Rodeghero et al conducted an eye-tracking study [17] to determine the statements and terms (i.e., identifier names) that programmers view as important when they summarize a method.…”
Section: Introductionmentioning
confidence: 99%