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

Lingfeng Bao, Zhenchang Xing, Xin Xia, David Lo, Shanping Li

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

37 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationProceedings - ACM'17 14th International Conference on Mining Software Repositories, MSR 2017
Subtitle of host publication20–21 May 2017 Buenos Aires, Argentina
EditorsAbram Hindle, Lin Tan
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages12
ISBN (Electronic)9781538615447, 9781538615454
Publication statusPublished - 2017
Externally publishedYes
EventIEEE International Working Conference on Mining Software Repositories 2017 - Buenos Aires, Argentina
Duration: 20 May 201721 May 2017
Conference number: 14th
https://ieeexplore.ieee.org/xpl/conhome/7959735/proceeding (Proceedings)


ConferenceIEEE International Working Conference on Mining Software Repositories 2017
Abbreviated titleMSR 2017
CityBuenos Aires
Internet address


  • Developer turnover
  • Mining software repositories
  • Prediction model

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