Improving topic model source code summarization

McBurney, Paul W.; Liu, Cheng; McMillan, Collin; Weninger, Tim

doi:10.1145/2597008.2597793

Cited by 39 publications

(20 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Few researches are found which have used NLP based techniques to create the extractive summaries. Paul W. McBurney et al [22] have used Topic Models to create the source code summaries focusing on the presentation of summaries. They used HDTM algorithm extract the hierarchy of topics from the source code.…”

Section: Extractive Summarization: An Overview In the Context Of Softmentioning

confidence: 99%

“…Even though it can be used for generating abstractive summaries as well but most of the researches are for extractive summaries only. [22] Source Code Topic Models(HDTM) JHotdraw, jajuk, jEdit, jTopas, nanoXML, siena Fowkes et al [24] Source Code Topic Models (extension of TopicSum)…”

Section: Extractive Summarization: An Overview In the Context Of Softmentioning

confidence: 99%

“…They evaluated their summaries based on accuracy, content adequacy and conciseness. McBurney et al [22] in other paper used human based intrinsic evaluation by recruiting 3 participants to find out the accuracy and effectiveness of the extractive summaries generated for source code of a project.…”

Section: Pyramid Score=a \ Bmentioning

confidence: 99%

“…It is determining if users can make accurate decisions with generated summaries. Reading Comprehension task is also used for extrinsic Rastkar et al [14] Source Code Intrinsic (F-Score) Nazar et al [16] Source Code Intrinsic (ROC, AUC, Precision, Recall, FMeasure) Ying et al [17] Source Code Intrinsic (R-Precision, ROC) Guerrouj et al [18] Source Code from StackOverflow Sites Intrinsic (Content Similarity, ROUGE) Mani et al [19] Bug Reports Intrinsic Ferreira et al [20] Bug Reports Intrinsic (Precision, Recall, F-Score) McBurney et al [22] Source Code Intrinsic (Human Evaluation) Accuracy, Effectiveness Badihi et al [24] Source Code Intrinsic (Precision, FScore)…”

Section: Extrinsic Evaluationmentioning

confidence: 99%

See 3 more Smart Citations

Summarization of Software Artifacts : A Review

Gupta¹,

S.K²

2017

IJCSIT

View full text Add to dashboard Cite

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.

show abstract

Section: Extractive Summarization: An Overview In the Context Of Softmentioning

confidence: 99%

Section: Extractive Summarization: An Overview In the Context Of Softmentioning

confidence: 99%

Section: Pyramid Score=a \ Bmentioning

confidence: 99%

Section: Extrinsic Evaluationmentioning

confidence: 99%

See 2 more Smart Citations

Summarization of Software Artifacts : A Review

Gupta¹,

S.K²

2017

IJCSIT

View full text Add to dashboard Cite

show abstract

“…22 * is "equal to" this StatusLine */ 23 public boolean equals(Object obj){ 24 try { 25 StatusLine r = (StatusLine) obj; 26 if (r.getCode() == getCode()&& 27 r.getReason().equals(getReason())) 28 return true; Previous work in code summarization has considered summarization using: (a) program slicing (i.e. hiding irrelevant lines of code for a chosen program path) [10], [11]; (b) natural language paraphrases [12], [13]; (c) short lists of keywords [14]- [17]; or (d) (potentially discontiguous) lines of code that match a user's query [18]. In contrast, our work is based on the idea that an effective summary can be obtained by carefully folding the original filesummarizing code with code.…”

Section: Introductionmentioning

confidence: 99%

Autofolding for Source Code Summarization

Fowkes

Chanthirasegaran

Ranca³

et al. 2017

IIEEE Trans. Software Eng.

View full text Add to dashboard Cite

Abstract-Developers spend much of their time reading and browsing source code, raising new opportunities for summarization methods. Indeed, modern code editors provide code folding, which allows one to selectively hide blocks of code. However this is impractical to use as folding decisions must be made manually or based on simple rules. We introduce the autofolding problem, which is to automatically create a code summary by folding less informative code regions. We present a novel solution by formulating the problem as a sequence of AST folding decisions, leveraging a scoped topic model for code tokens. On an annotated set of popular open source projects, we show that our summarizer outperforms simpler baselines, yielding a 28% error reduction. Furthermore, we find through a case study that our summarizer is strongly preferred by experienced developers. More broadly, we hope this work will aid program comprehension by turning code folding into a usable and valuable tool.

show abstract

Automated feature discovery via sentence selection and source code summarization

McBurney

Liu

McMillan

2016

J. Softw. Evol. and Proc.

Self Cite

View full text Add to dashboard Cite

Programs are, in essence, a collection of implemented features. Feature discovery in software engineering is the task of identifying key functionalities that a program implements. Manual feature discovery can be time consuming and expensive, leading to automatic feature discovery tools being developed. However, these approaches typically only describe features using lists of keywords, which can be difficult for readers who are not already familiar with the source code. An alternative to keyword lists is sentence selection, in which one sentence is chosen from among the sentences in a text document to describe that document. Sentence selection has been widely studied in the context of natural language summarization but is only beginning to be explored as a solution to feature discovery. In this paper, we compare four sentence selection strategies for the purpose of feature discovery. Two are off-the-shelf approaches, while two are adaptations we propose. We present our findings as guidelines and recommendations to designers of feature discovery tools.

show abstract

Improving topic model source code summarization

Cited by 39 publications

References 25 publications

Summarization of Software Artifacts : A Review

Summarization of Software Artifacts : A Review

Autofolding for Source Code Summarization

Automated feature discovery via sentence selection and source code summarization

Contact Info

Product

Resources

About