An Eye-Tracking Study of Java Programmers and Application to Source Code Summarization

Rodeghero, Paige; Liu, Cheng; McBurney, Paul W.; McMillan, Collin

doi:10.1109/tse.2015.2442238

Cited by 58 publications

(32 citation statements)

References 67 publications

(107 reference statements)

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“…Much earlier works, such as Haiduc [64], implemented summarization by text mining and text retrieval methods. Some papers [65], [66] also researched on heuristic-based and natural language techniques to provide comments for source code of small functions/methods.…”

Section: Code Summarizationmentioning

confidence: 99%

CORE: Automating Review Recommendation for Code Changes

Siow

Gao

Fan

et al. 2020

2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER)

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Code review is a common process that is used by developers, in which a reviewer provides useful comments or points out defects in the submitted source code changes via pull request. Code review has been widely used for both industry and open-source projects due to its capacity in early defect identification, project maintenance, and code improvement. With rapid updates on project developments, code review becomes a non-trivial and labor-intensive task for reviewers. Thus, an automated code review engine can be beneficial and useful for project development in practice. Although there exist prior studies on automating the code review process by adopting static analysis tools or deep learning techniques, they often require external sources such as partial or full source code for accurate review suggestion. In this paper, we aim at automating the code review process only based on code changes and the corresponding reviews but with better performance.The hinge of accurate code review suggestion is to learn good representations for both code changes and reviews. To achieve this with limited source, we design a multi-level embedding (i.e., word embedding and character embedding) approachto represent the semantics provided by code changes and reviews.The embeddings are then well trained through a proposed attentional deep learning model, as a whole named CORE. We evaluate the effectiveness of CORE on code changes and reviews collected from 19 popular Java projects hosted on Github. Experimental results show that our model CORE can achieve significantly better performance than the state-of-the-art model (DeepMem), with an increase of 131.03% in terms of Recall@10 and 150.69% in terms of Mean Reciprocal Rank. Qualitative general word analysis among project developers also demonstrates the performance of CORE in automating code review.

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Section: Code Summarizationmentioning

confidence: 99%

CORE: Automating Review Recommendation for Code Changes

Siow

Gao

Fan

et al. 2020

2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER)

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“…The term "source code summarization" was coined around 2009 by Haiduc et al [1], [17] to describe the task of generating short descriptions of code. Referring to the process as "summarization" alludes to a history of work in Natural Language Processing of extractive summarization of documents -early attempts at code summarization involved choosing a set of n important words from code [18], [19] and then converting those words into complete sentences by placing them into sentence templates [2], [20]- [22]. A 2016 survey [23] highlights these approaches around the time that a vast majority of code summarization techniques began to be based on neural networks trained from big data input [10], [14], [24]- [27].…”

Section: A Source Code Summarizationmentioning

confidence: 99%

Automatically Extracting Subroutine Summary Descriptions from Unstructured Comments

Eberhart

LeClair

McMillan

2020

2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER)

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descriptions of subroutines are short (usually one-sentence) natural language explanations of a subroutine's behavior and purpose in a program. These summaries are ubiquitous in documentation, and many tools such as JavaDocs and Doxygen generate documentation built around them. And yet, extracting summaries from unstructured source code repositories remains a difficult research problem -it is very difficult to generate clean structured documentation unless the summaries are annotated by programmers. This becomes a problem in large repositories of legacy code, since it is cost prohibitive to retroactively annotate summaries in dozens or hundreds of old programs. Likewise, it is a problem for creators of automatic documentation generation algorithms, since these algorithms usually must learn from large annotated datasets, which do not exist for many programming languages. In this paper, we present a semi-automated approach via crowdsourcing and a fully-automated approach for annotating summaries from unstructured code comments. We present experiments validating the approaches, and provide recommendations and cost estimates for automatically annotating large repositories.

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“…Rodeghero et al [25] have generated the extractive summaries of java methods by finding out the important keywords based on eye tracking study of 10 java professionals where they were asked to read and write the summaries especially for those methods which were uncommented by focusing on eye-movements, gaze fixations and regressions.…”

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

confidence: 99%

“…Storm, elasticSearch, Spring-framework, libgdx, bigbluebutton, netty Rodeghero et al [25] Source Code Eye-tracking interactions…”

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

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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An Eye-Tracking Study of Java Programmers and Application to Source Code Summarization

Cited by 58 publications

References 67 publications

CORE: Automating Review Recommendation for Code Changes

CORE: Automating Review Recommendation for Code Changes

Automatically Extracting Subroutine Summary Descriptions from Unstructured Comments

Summarization of Software Artifacts : A Review

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