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Extracting Knowledge From On-Line Sources for Software Engineering Labor Market: A Mapping Study
- Source :
- IEEE Access, Vol 7, Pp 157595-157613 (2019)
- Publication Year :
- 2019
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2019.
-
Abstract
- Software engineering is a continuously evolving sector and the demands of the related labor market result in a wide variety of job openings, ranging from developers to customer service positions. Thus, there is a need to continuously monitor labor market trends using data and analytics. Both employers and employees can benefit by capturing emerging trends which can facilitate continuous learning and training in new technologies, support of better matching between a job offer and the ideal candidate and expertise detection. To fulfill these needs, the results of labor market analytics need to reach the stakeholders timely and accurately. However, often delays occur, which stem from time-consuming approaches based on collecting data from traditional sources, such as questionnaires or interviews. Recently, researchers started leveraging content from digital sources, which are easily accessed and contain a wealth of information. This paper presents the results of a Systematic Mapping Study on digital sources that can be used to address the data analytics needs of the labor market. It provides a multifaceted categorization of the issues involved in the analysis of digital sources of the software engineering labor market. It aims to identify digital labor market sources for data retrieval which are appropriate for employers and employees analytics. Additionally, it aims to connect different skill types, needs and goals of labor market with the utilization of digital sources and data analysis methods. In total 86 primary studies were selected and each one was evaluated and classified aiming to identify the: (a) digital sources that are used for labor market analytics; (b) type of skills they examine; (c) methods which are used to utilize the raw digital content; (d) goals for which every primary study is conducted; (e) beneficiaries (stakeholder) of the results; and (f) time trends for all the above.
- Subjects :
- General Computer Science
Emerging technologies
Computer science
Digital content
Knowledge engineering
Beneficiary
02 engineering and technology
Software
Human factor
020204 information systems
0202 electrical engineering, electronic engineering, information engineering
General Materials Science
digital sources
skills
Digital labor
business.industry
labor market analytics
General Engineering
Stakeholder
020207 software engineering
Variety (cybernetics)
Market research
Analytics
lcsh:Electrical engineering. Electronics. Nuclear engineering
business
Software engineering
lcsh:TK1-9971
software engineering
Subjects
Details
- ISSN :
- 21693536
- Volume :
- 7
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....48296d4233a5c2ed69eadda94011dbba