Evaluating Automatic Spreadsheet Metadata Extraction on a Large Set of Responses from MOOC Participants

Evaluating Automatic Spreadsheet Metadata Extraction on a Large Set of Responses from MOOC Participants

Roy, S. (TU Delft Software Engineering)
Hermans, F.F.J. (TU Delft Software Engineering)
Aivaloglou, E.A. (TU Delft Software Engineering)
Winter, J.
van Deursen, A. (TU Delft Software Technology)

Jiu, A. (editor)

Software Technology

2016

Spreadsheets are popular end-user computing applications and one reason behind their popularity is that they offer a large degree of freedom to their users regarding the way they can structure their data. However, this flexibility also makes spreadsheets difficult to understand. Textual documentation can address this issue, yet for supporting automatic generation of textual documentation, an important pre-requisite is to extract metadata inside spreadsheets. It is a challenge though, to distinguish between data and metadata due to the lack of universally accepted structural patterns in spreadsheets. Two existing approaches for automatic extraction of spreadsheet metadata were not evaluated on large datasets consisting of user inputs. Hence in this paper, we describe the collection of a large number of user responses regarding identification of spreadsheet metadata from participants of a MOOC. We describe the use of this large dataset to understand how users identify metadata in spreadsheets, and to evaluate two existing approaches of automatic metadata extraction from spreadsheets. The results provide us with directions to follow in order to improve metadata extraction approaches, obtained from insights about user perception of metadata. We also understand what type of spreadsheet patterns the existing approaches perform well and on what type poorly, and thus which problem areas to focus on in order to improve.

computer aided instruction
meta data
personal computing
spreadsheet programs
text analysis
MOOC participants
automatic spreadsheet metadata extraction
automatic textual documentation generation
data structure
end-user computing applications
metadata user perception
spreadsheet patterns
Computers
Conferences
Data mining
Documentation
Metadata
Reliability
Software
Empirical evaluation
MOOC
Meta-data extraction
Spreadsheet
User-study

http://resolver.tudelft.nl/uuid:34045a34-02db-4441-8846-b9f094680d7d

https://doi.org/10.1109/SANER.2016.98

IEEE Society, Los Alamitos, CA

978-1-5090-1855-0

2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER), 2

SANER 2016, 2016-03-14 → 2016-03-18, Osaka, Japan

Institutional Repository

conference paper

© 2016 S. Roy, F.F.J. Hermans, E.A. Aivaloglou, J. Winter, A. van Deursen