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Including pervasive web content in evidence-based software engineering : A case study

Ma, Jinyu; Li, Zheng LU and Liu, Yan LU (2018) 24th Asia-Pacific Software Engineering Conference Workshops, APSECW 2017 2018-January. p.55-62
Abstract

Context: Both scientific publications and grey literature have widely been employed as sources of empirical evidence in evidence-based software engineering (EBSE). However, there is still a fierce debate about whether or not the pervasive Web content can act as an alternative means to gather evidence for EBSE. Aim: To help ourselves enter this debate, this work aims to obtain some pre-evidence of reviewing Web documents for verifying the value and reliability of online materials. Method: Given the unique characteristics of Web content, we adapted the traditional Systematic Literature Review (SLR) methodology in EBSE, and conducted a review case study in the deep learning domain. Results: Our study selected four different search sources... (More)

Context: Both scientific publications and grey literature have widely been employed as sources of empirical evidence in evidence-based software engineering (EBSE). However, there is still a fierce debate about whether or not the pervasive Web content can act as an alternative means to gather evidence for EBSE. Aim: To help ourselves enter this debate, this work aims to obtain some pre-evidence of reviewing Web documents for verifying the value and reliability of online materials. Method: Given the unique characteristics of Web content, we adapted the traditional Systematic Literature Review (SLR) methodology in EBSE, and conducted a review case study in the deep learning domain. Results: Our study selected four different search sources and captured 5082 'deep learning'-relevant Web documents. After a set of thematic synthesis steps ranging from keyword identification to brainstorming, the collected raw data were eventually evolved into a mind map of six semantic topics. Conclusions: We confirm that Web content can provide valuable information as supplementary evidence in EBSE. However, reviewing Web content introduces more search source bias rather than academic publications' location bias that is due to factors like ease of access or indexing levels in digital libraries.

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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Deep Learning, Evidence-based Software Engineering, Systematic Literature Review, Topic Modeling, Web Content
host publication
Proceedings - 2017 24th Asia-Pacific Software Engineering Conference Workshops, APSECW 2017
volume
2018-January
pages
8 pages
publisher
Institute of Electrical and Electronics Engineers Inc.
conference name
24th Asia-Pacific Software Engineering Conference Workshops, APSECW 2017
conference location
Nanjing, China
conference dates
2017-12-04 - 2017-12-08
external identifiers
  • scopus:85050615349
ISBN
9781538626498
DOI
10.1109/APSECW.2017.12
language
English
LU publication?
yes
id
af36ab2c-6cdb-4948-92c4-ce10a9d69255
date added to LUP
2018-09-17 15:30:06
date last changed
2019-02-20 11:27:09
@inproceedings{af36ab2c-6cdb-4948-92c4-ce10a9d69255,
  abstract     = {<p>Context: Both scientific publications and grey literature have widely been employed as sources of empirical evidence in evidence-based software engineering (EBSE). However, there is still a fierce debate about whether or not the pervasive Web content can act as an alternative means to gather evidence for EBSE. Aim: To help ourselves enter this debate, this work aims to obtain some pre-evidence of reviewing Web documents for verifying the value and reliability of online materials. Method: Given the unique characteristics of Web content, we adapted the traditional Systematic Literature Review (SLR) methodology in EBSE, and conducted a review case study in the deep learning domain. Results: Our study selected four different search sources and captured 5082 'deep learning'-relevant Web documents. After a set of thematic synthesis steps ranging from keyword identification to brainstorming, the collected raw data were eventually evolved into a mind map of six semantic topics. Conclusions: We confirm that Web content can provide valuable information as supplementary evidence in EBSE. However, reviewing Web content introduces more search source bias rather than academic publications' location bias that is due to factors like ease of access or indexing levels in digital libraries.</p>},
  author       = {Ma, Jinyu and Li, Zheng and Liu, Yan},
  isbn         = {9781538626498},
  keyword      = {Deep Learning,Evidence-based Software Engineering,Systematic Literature Review,Topic Modeling,Web Content},
  language     = {eng},
  location     = {Nanjing, China},
  month        = {03},
  pages        = {55--62},
  publisher    = {Institute of Electrical and Electronics Engineers Inc.},
  title        = {Including pervasive web content in evidence-based software engineering : A case study},
  url          = {http://dx.doi.org/10.1109/APSECW.2017.12},
  volume       = {2018-January},
  year         = {2018},
}