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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

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...[Show more]

dc.contributor.authorBao, Lingfeng
dc.contributor.authorXing, Zhenchang
dc.contributor.authorXia, Xin
dc.contributor.authorLo, David
dc.contributor.authorLi, Shanping
dc.coverage.spatialBuenos Aires, Argentina
dc.date.accessioned2021-10-14T21:47:55Z
dc.date.createdMay 20-21 2017
dc.identifier.isbn9781538615447
dc.identifier.urihttp://hdl.handle.net/1885/250838
dc.description.abstractSoftware 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.
dc.description.sponsorshipThis research was supported by NSFC Program (No. 61602403 and 61572426), and National Key Technology R&D Program of the Ministry of Science and Technology of China (No. 2015BAH17F01).
dc.format.mimetypeapplication/pdf
dc.language.isoen_AU
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE Inc)
dc.relation.ispartofseries14th IEEE/ACM International Conference on Mining Software Repositories, MSR 2017
dc.rights© 2017 IEEE
dc.sourceIEEE International Working Conference on Mining Software Repositories
dc.subjectdeveloper turnover
dc.subjectprediction model
dc.subjectmining software repositories
dc.titleWho Will Leave the Company?: A Large-Scale Industry Study of Developer Turnover by Mining Monthly Work Report
dc.typeConference paper
local.description.notesImported from ARIES
local.description.refereedYes
dc.date.issued2017
local.identifier.absfor010499 - Statistics not elsewhere classified
local.identifier.ariespublicationa383154xPUB7715
local.publisher.urlhttps://www.ieee.org/
local.type.statusPublished Version
local.contributor.affiliationBao, Lingfeng, Zhejiang University
local.contributor.affiliationXing, Zhenchang, College of Engineering and Computer Science, ANU
local.contributor.affiliationXia, Xin, University of British Columbia
local.contributor.affiliationLo, David, Singapore Management University
local.contributor.affiliationLi, Shanping, Zhejiang University
local.description.embargo2099-12-31
local.bibliographicCitation.startpage170
local.bibliographicCitation.lastpage181
local.identifier.doi10.1109/MSR.2017.58
dc.date.updated2020-11-23T11:28:02Z
local.identifier.scopusID2-s2.0-85026554861
CollectionsANU Research Publications

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