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|Title:||A mapping study on mining software process|
|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 Place:||NJ, USA|
|Series/Report no.:||Asia-Pacific Software Engineering Conference|
|Conference Name:||24th Asia-Pacific Software Engineering Conference (APSEC 2017) (04 Dec 2017 - 08 Dec 2017 : Nanjing, CHINA)|
|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|
|Appears in Collections:||Computer Science publications|
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