Quantifying and Mitigating Turnover-Induced Knowledge Loss: Case Studies of Chrome and a Project at Avaya

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...

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Veröffentlicht in:Proceedings / International Conference on Software Engineering S. 1006 - 1016
Hauptverfasser: Rigby, Peter C., Yue Cai Zhu, Donadelli, Samuel M., Mockus, Audris
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: ACM 01.05.2016
Schlagworte:
Adaptation models
Knowledge Distribution
Loss measurement
Mining Software Repositories
Monte Carlo methods
Productivity
Quantitative Risk Management
Risk management
Software
Software engineering
Successors
Truck Factor
Turnover
ISSN:1558-1225
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Zusammenfassung: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.
ISSN:1558-1225
DOI:10.1145/2884781.2884851