Motivation Research Using Labeling Functions

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

Abstract

Motivation is an important factor in software development. However, it is a subjective concept that is hard to quantify and study empirically. In order to use the wealth of data available about real software development projects in GitHub, we represent the motivation of developers using labeling functions. These are validated heuristics that need only be better than a guess, computable on a dataset. We define four labeling functions for motivation based on behavioral cues like working in diverse hours of the day. We validated the functions by agreement with respect to a developers survey, per person behavior, and temporal changes. We then apply them to 150 thousand developers working on GitHub projects. Using the identification of motivated developers, we measure developer performance gaps. We show that motivated developers have up to 70% longer activity period, produce up to 300% more commits, and invest up to 44% more time per commit.

Original languageEnglish
Title of host publicationProceedings of 2024 28th International Conference on Evaluation and Assessment in Software Engineering, EASE 2024
PublisherAssociation for Computing Machinery
Pages222-231
Number of pages10
ISBN (Electronic)9798400717017
StatePublished - 18 Jun 2024
Event28th International Conference on Evaluation and Assessment in Software Engineering, EASE 2024 - Salerno, Italy
Duration: 18 Jun 202421 Jun 2024

Publication series

NameACM International Conference Proceeding Series

Conference

Conference28th International Conference on Evaluation and Assessment in Software Engineering, EASE 2024
Country/TerritoryItaly
CitySalerno
Period18/06/2421/06/24

Bibliographical note

Publisher Copyright:
© 2024 Owner/Author.

Keywords

  • methodology
  • motivation
  • software engineering
  • weak supervision

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