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Combining self-reported and automatic data to improve programming effort measurement
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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 table of contents
Lisbon, Portugal
SESSION: Effort estimation table of contents
Pages: 356 - 365  
Year of Publication: 2005
ISBN:1-59593-014-0
Also published in ...
Authors
Lorin Hochstein  University of Maryland, College Park, MD
Victor R. Basili  University of Maryland, College Park, MD and Fraunhofer Center, College Park, MD
Marvin V. Zelkowitz  University of Maryland, College Park, MD and Fraunhofer Center, College Park, MD
Jeffrey K. Hollingsworth  University of Maryland, College Park, MD
Jeff Carver  Mississippi State University, Mississippi State, MS
Sponsors
SIGSOFT: ACM Special Interest Group on Software Engineering
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
Bibliometrics
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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Collaborative Colleagues:
Lorin Hochstein: colleagues
Victor R. Basili: colleagues
Marvin V. Zelkowitz: colleagues
Jeffrey K. Hollingsworth: colleagues
Jeff Carver: colleagues