| Combining self-reported and automatic data to improve programming effort measurement |
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Foundations of Software Engineering
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Proceedings of the 10th European software engineering conference held jointly with 13th ACM SIGSOFT international symposium on Foundations of software engineering
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Lisbon, Portugal
SESSION: Effort estimation
table of contents
Pages: 356 - 365
Year of Publication: 2005
ISBN:1-59593-014-0
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Authors
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Lorin Hochstein
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University of Maryland, College Park, MD
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Victor R. Basili
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University of Maryland, College Park, MD and Fraunhofer Center, College Park, MD
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Marvin V. Zelkowitz
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University of Maryland, College Park, MD and Fraunhofer Center, College Park, MD
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Jeffrey K. Hollingsworth
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University of Maryland, College Park, MD
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Jeff Carver
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Mississippi State University, Mississippi State, MS
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Downloads (6 Weeks): 3, Downloads (12 Months): 42, Citation Count: 3
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ABSTRACT
Measuring effort accurately and consistently across subjects in a programming experiment can be a surprisingly difficult task. In particular, measures based on self-reported data may differ significantly from measures based on data which is recorded automatically from a subject's computing environment. Since self-reports can be unreliable, and not all activities can be captured automatically, a complete measure of programming effort should incorporate both classes of data. In this paper, we show how self-reported and automatic effort can be combined to perform validation and to measure total programming effort.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
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CITED BY 3
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Jun H. Kim , Daniel V. Gunn , Eric Schuh , Bruce Phillips , Randy J. Pagulayan , Dennis Wixon, Tracking real-time user experience (TRUE): a comprehensive instrumentation solution for complex systems, Proceeding of the twenty-sixth annual SIGCHI conference on Human factors in computing systems, April 05-10, 2008, Florence, Italy
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