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Quantifying and mitigating turnover-induced knowledge loss: case studies of chrome and a project at avaya

Published: 14 May 2016 Publication History

Abstract

The utility of source code, as of other knowledge artifacts, is predicated on the existence of individuals skilled enough to derive value by using or improving it. Developers leaving a software project deprive the project of the knowledge of the decisions they have made. Previous research shows that the survivors and newcomers maintaining abandoned code have reduced productivity and are more likely to make mistakes. We focus on quantifying the extent of abandoned source files and adapt methods from financial risk analysis to assess the susceptibility of the project to developer turnover. In particular, we measure the historical loss distribution and find (1) that projects are susceptible to losses that are more than three times larger than the expected loss. Using historical simulations we find (2) that projects are susceptible to large losses that are over five times larger than the expected loss. We use Monte Carlo simulations of disaster loss scenarios and find (3) that simplistic estimates of the 'truck factor' exaggerate the potential for loss. To mitigate loss from developer turnover, we modify Cataldo et al.'s coordination requirements matrices. We find (4) that we can recommend the correct successor 34% to 48% of the time. We also find that having successors reduces the expected loss by as much as 15%. Our approach helps large projects assess the risk of turnover thereby making risk more transparent and manageable.

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cover image ACM Conferences
ICSE '16: Proceedings of the 38th International Conference on Software Engineering
May 2016
1235 pages
ISBN:9781450339001
DOI:10.1145/2884781
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 14 May 2016

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Author Tags

  1. knowledge distribution
  2. mining software repositories
  3. quantitative risk management
  4. successors
  5. truck factor
  6. turnover

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  • (2024)The Classics Never Go Out of Style: An Empirical Study of Downgrades from the Bazel Build TechnologyProceedings of the IEEE/ACM 46th International Conference on Software Engineering10.1145/3597503.3639169(1-12)Online publication date: 20-May-2024
  • (2024)Characterizing the Prevalence, Distribution, and Duration of Stale Reviewer RecommendationsIEEE Transactions on Software Engineering10.1109/TSE.2024.342236950:8(2096-2109)Online publication date: Aug-2024
  • (2024)Bringing Open Source Communication and Development Together: A Cross-Platform Study on Gitter and GitHubIEEE Transactions on Software Engineering10.1109/TSE.2024.341029250:11(2807-2826)Online publication date: 1-Nov-2024
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