Who will leave the company?: A large-scale industry study of developer turnover by mining monthly work report

Software developer turnover has become a big challenge for information technology (IT) companies. The departure of key software developers might cause big loss to an IT company since they also depart with important business knowledge and critical technical skills. Understanding developer turnover is...

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Main Authors: BAO, Lingfeng, XING, Zhenchang, XIA, Xin, LO, David, LI, Shanping
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2017
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Online Access:https://ink.library.smu.edu.sg/sis_research/3696
https://ink.library.smu.edu.sg/context/sis_research/article/4698/viewcontent/p170_bao__1_.pdf
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spelling sg-smu-ink.sis_research-46982020-06-09T02:42:33Z Who will leave the company?: A large-scale industry study of developer turnover by mining monthly work report BAO, Lingfeng XING, Zhenchang XIA, Xin LO, David LI, Shanping Software developer turnover has become a big challenge for information technology (IT) companies. The departure of key software developers might cause big loss to an IT company since they also depart with important business knowledge and critical technical skills. Understanding developer turnover is very important for IT companies to retain talented developers and reduce the loss due to developers' departure. Previous studies mainly perform qualitative observations or simple statistical analysis of developers' activity data to understand developer turnover. In this paper, we investigate whether we can predict the turnover of software developers in non-open source companies by automatically analyzing monthly self-reports. The monthly work reports in our study are from two IT companies. Monthly reports in these two companies are used to report a developer's activities and working hours in a month. We would like to investigate whether a developer will leave the company after he/she enters company for one year based on his/her first six monthly reports. To perform our prediction, we extract many factors from monthly reports, which are grouped into 6 dimensions. We apply several classifiers including naive Bayes, SVM, decision tree, kNN and random forest. We conduct an experiment on about 6-years monthly reports from two companies, this data contains 3,638 developers over time. We find that random forest classifier achieves the best performance with an F1-measure of 0.86 for retained developers and an F1-measure of 0.65 for not-retained developers. We also investigate the relationship between our proposed factors and developers' departure, and the important factors that indicate a developer's departure. We find the content of task report in monthly reports, the standard deviation of working hours, and the standard deviation of working hours of project members in the first month are the top three important factors. 2017-05-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/3696 info:doi/10.1109/MSR.2017.58 https://ink.library.smu.edu.sg/context/sis_research/article/4698/viewcontent/p170_bao__1_.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Companies Software Feature extraction Data mining Standards Computer science Human Resources Management Numerical Analysis and Scientific Computing Software Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Companies
Software
Feature extraction
Data mining
Standards
Computer science
Human Resources Management
Numerical Analysis and Scientific Computing
Software Engineering
spellingShingle Companies
Software
Feature extraction
Data mining
Standards
Computer science
Human Resources Management
Numerical Analysis and Scientific Computing
Software Engineering
BAO, Lingfeng
XING, Zhenchang
XIA, Xin
LO, David
LI, Shanping
Who will leave the company?: A large-scale industry study of developer turnover by mining monthly work report
description Software developer turnover has become a big challenge for information technology (IT) companies. The departure of key software developers might cause big loss to an IT company since they also depart with important business knowledge and critical technical skills. Understanding developer turnover is very important for IT companies to retain talented developers and reduce the loss due to developers' departure. Previous studies mainly perform qualitative observations or simple statistical analysis of developers' activity data to understand developer turnover. In this paper, we investigate whether we can predict the turnover of software developers in non-open source companies by automatically analyzing monthly self-reports. The monthly work reports in our study are from two IT companies. Monthly reports in these two companies are used to report a developer's activities and working hours in a month. We would like to investigate whether a developer will leave the company after he/she enters company for one year based on his/her first six monthly reports. To perform our prediction, we extract many factors from monthly reports, which are grouped into 6 dimensions. We apply several classifiers including naive Bayes, SVM, decision tree, kNN and random forest. We conduct an experiment on about 6-years monthly reports from two companies, this data contains 3,638 developers over time. We find that random forest classifier achieves the best performance with an F1-measure of 0.86 for retained developers and an F1-measure of 0.65 for not-retained developers. We also investigate the relationship between our proposed factors and developers' departure, and the important factors that indicate a developer's departure. We find the content of task report in monthly reports, the standard deviation of working hours, and the standard deviation of working hours of project members in the first month are the top three important factors.
format text
author BAO, Lingfeng
XING, Zhenchang
XIA, Xin
LO, David
LI, Shanping
author_facet BAO, Lingfeng
XING, Zhenchang
XIA, Xin
LO, David
LI, Shanping
author_sort BAO, Lingfeng
title Who will leave the company?: A large-scale industry study of developer turnover by mining monthly work report
title_short Who will leave the company?: A large-scale industry study of developer turnover by mining monthly work report
title_full Who will leave the company?: A large-scale industry study of developer turnover by mining monthly work report
title_fullStr Who will leave the company?: A large-scale industry study of developer turnover by mining monthly work report
title_full_unstemmed Who will leave the company?: A large-scale industry study of developer turnover by mining monthly work report
title_sort who will leave the company?: a large-scale industry study of developer turnover by mining monthly work report
publisher Institutional Knowledge at Singapore Management University
publishDate 2017
url https://ink.library.smu.edu.sg/sis_research/3696
https://ink.library.smu.edu.sg/context/sis_research/article/4698/viewcontent/p170_bao__1_.pdf
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