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Contribution Details
| Type | Book Chapter |
| Scope | Discipline-based scholarship |
| Title | Measuring individual productivity |
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| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
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| Booktitle | Perspectives on Data Science for Software Engineering |
| ISBN | 978-0128042069 |
| Place of Publication | Burlington, Massachusetts |
| Publisher | Morgan Kaufmann |
| Page Range | 67 - 71 |
| Date | 2016 |
| Abstract Text | Measuring productivity of individual developers is challenging. In some domains, such as car manufacturing, specific outcome measures over time, such as the number of cars produced in a day, can work well to measure and incentivize productivity. However, the less clearly defined and more flexible process of software development makes it difficult, if not impossible, to define such measures. In particular, there is no single and simple best metric that can be used for all software developers and more individual combinations of measures are wanted and needed that also take into account the process and not just the final outcome. In this chapter, we will discuss some of the challenges and previous insights on the measuring of individual developer productivity. |
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| Other Identification Number | merlin-id:13565 |
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