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Measuring individual productivity


Fritz, Thomas (2016). Measuring individual productivity. In: Menzies, Tim; Williams, Laurie; Zimmermann, Thomas. Perspectives on Data Science for Software Engineering. Burlington, Massachusetts: Morgan Kaufmann, 67-71.

Abstract

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.

Abstract

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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Additional indexing

Item Type:Book Section, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Date:2016
Deposited On:08 Aug 2016 13:21
Last Modified:08 Aug 2016 13:21
Publisher:Morgan Kaufmann
ISBN:978-0128042069
Related URLs:http://store.elsevier.com/Perspectives-on-Data-Science-for-Software-Engineering/Tim-Menzies/isbn-9780128042069/ (Publisher)
Other Identification Number:merlin-id:13565

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