Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/112621
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Type: Conference paper
Title: A mapping study on mining software process
Author: Dong, L.
Liu, B.
Li, Z.
Wu, O.
Babar, M.
Xue, B.
Citation: Proceedings of the 24th Asia-Pacific Software Engineering Conference (APSEC 2017), 2018 / Lv, J., Zhang, H., Hinchey, M., Liu, X. (ed./s), vol.2017-December, pp.51-60
Publisher: IEEE
Publisher Place: Piscataway, NJ
Issue Date: 2018
Series/Report no.: Asia-Pacific Software Engineering Conference
ISBN: 1538636824
9781538636824
ISSN: 1530-1362
Conference Name: 24th Asia-Pacific Software Engineering Conference (APSEC 2017) (04 Dec 2017 - 08 Dec 2017 : Nanjing, CHINA)
Statement of
Responsibility: 
Liming Dong, Bohan Liu, Zheng Li, Ou Wu, Muhammad Ali Babar, Bingbing Xue
Abstract: Background: Mining Software Process (MSP) helps distill important information about software process enactment from software data repositories. An increasing amount of research effort is being dedicated to MSP. These studies differ in various aspects (e.g., topics, data, and techniques) of MSP. Objective: We aim to study the state of the art on MSP from following aspects, i.e., research topics, data sources, data types, mining techniques, and mining tools. Method: We conducted a systematic mapping study on the research relevant to MSP at both microprocess and macroprocess levels. Results: Our mapping study identified 40 relevant studies that can be grouped into microprocess and macroprocess levels. The identified mining techniques have been mapped onto the associated mining tools that fall into four types. Driven by the three research questions which represented in a meta-model, the findings revealed the correlations among the research topics, data sources, data types, mining techniques, and mining tools. Conclusion: It is observed that in order to discover the software process model or map, the main data source is from industrial project. Current mining techniques for microprocess research are mostly business process mining or sequence mining techniques used to recover descriptive software process. In addition, various machine learning algorithms and novel proposed methods are used to improve the accuracy of macroprocess level factors (e.g., software effort estimation).
Keywords: Mapping study; software process; mining repository; software engineering
Rights: © 2017 IEEE
RMID: 0030085714
DOI: 10.1109/APSEC.2017.11
Published version: https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8305447
Appears in Collections:Computer Science publications

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