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Do estimators learn? On the effect of a positively skewed distribution of effort data on software portfolio productivity. / Huijgens, Hennie; Vogelezang, Frank.

WETSoM 2016 Proceedings of the 7th International Workshop on Emerging Trends in Software Metrics, . New York : Association for Computing Machinery (ACM), 2016. p. 8-14.

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Harvard

Huijgens, H & Vogelezang, F 2016, Do estimators learn? On the effect of a positively skewed distribution of effort data on software portfolio productivity. in WETSoM 2016 Proceedings of the 7th International Workshop on Emerging Trends in Software Metrics, . Association for Computing Machinery (ACM), New York, pp. 8-14, 7th International Workshop on Emerging Trends in Software Metrics, WETSoM 2016, Austin, United States, 14/05/16. https://doi.org/10.1145/2897695.2897698

APA

Huijgens, H., & Vogelezang, F. (2016). Do estimators learn? On the effect of a positively skewed distribution of effort data on software portfolio productivity. In WETSoM 2016 Proceedings of the 7th International Workshop on Emerging Trends in Software Metrics, (pp. 8-14). New York: Association for Computing Machinery (ACM). https://doi.org/10.1145/2897695.2897698

Vancouver

Huijgens H, Vogelezang F. Do estimators learn? On the effect of a positively skewed distribution of effort data on software portfolio productivity. In WETSoM 2016 Proceedings of the 7th International Workshop on Emerging Trends in Software Metrics, . New York: Association for Computing Machinery (ACM). 2016. p. 8-14 https://doi.org/10.1145/2897695.2897698

Author

Huijgens, Hennie ; Vogelezang, Frank. / Do estimators learn? On the effect of a positively skewed distribution of effort data on software portfolio productivity. WETSoM 2016 Proceedings of the 7th International Workshop on Emerging Trends in Software Metrics, . New York : Association for Computing Machinery (ACM), 2016. pp. 8-14

BibTeX

@inproceedings{257ec276e73e48749047092ba68c0a91,
title = "Do estimators learn?: On the effect of a positively skewed distribution of effort data on software portfolio productivity",
abstract = "We study whether an assumed positively skewed distribution of effort data prevents software estimators to learn over time; leading to increasing differences between planned and actual effort and a deteriorating (worsening) trend on productivity. We analyze data of 25 software releases of one application, collected over a period of six years in a public sector institution in The Netherlands. We statistically test for distribution, trend on differences between planned versus actual effort over time, and productivity of software portfolios. The key contributions of this paper are that we show that a proposed assumption that assumes any relation between a positively skewed distribution of effort data and a deteriorating productivity is not applicable to the subject dataset. We find that the effort data is to be characterized as positively skewed distributed, and we do see a shift over time from under-estimation to over-estimation. We do not find evidence for a deteriorating productivity; on the contrary productivity improves over time, indicating that estimators in the subject organization did learn.",
keywords = "Function Point Analysis, Software Economics, Software Estimation",
author = "Hennie Huijgens and Frank Vogelezang",
year = "2016",
doi = "10.1145/2897695.2897698",
language = "English",
pages = "8--14",
booktitle = "WETSoM 2016 Proceedings of the 7th International Workshop on Emerging Trends in Software Metrics,",
publisher = "Association for Computing Machinery (ACM)",
address = "United States",

}

RIS

TY - GEN

T1 - Do estimators learn?

T2 - On the effect of a positively skewed distribution of effort data on software portfolio productivity

AU - Huijgens, Hennie

AU - Vogelezang, Frank

PY - 2016

Y1 - 2016

N2 - We study whether an assumed positively skewed distribution of effort data prevents software estimators to learn over time; leading to increasing differences between planned and actual effort and a deteriorating (worsening) trend on productivity. We analyze data of 25 software releases of one application, collected over a period of six years in a public sector institution in The Netherlands. We statistically test for distribution, trend on differences between planned versus actual effort over time, and productivity of software portfolios. The key contributions of this paper are that we show that a proposed assumption that assumes any relation between a positively skewed distribution of effort data and a deteriorating productivity is not applicable to the subject dataset. We find that the effort data is to be characterized as positively skewed distributed, and we do see a shift over time from under-estimation to over-estimation. We do not find evidence for a deteriorating productivity; on the contrary productivity improves over time, indicating that estimators in the subject organization did learn.

AB - We study whether an assumed positively skewed distribution of effort data prevents software estimators to learn over time; leading to increasing differences between planned and actual effort and a deteriorating (worsening) trend on productivity. We analyze data of 25 software releases of one application, collected over a period of six years in a public sector institution in The Netherlands. We statistically test for distribution, trend on differences between planned versus actual effort over time, and productivity of software portfolios. The key contributions of this paper are that we show that a proposed assumption that assumes any relation between a positively skewed distribution of effort data and a deteriorating productivity is not applicable to the subject dataset. We find that the effort data is to be characterized as positively skewed distributed, and we do see a shift over time from under-estimation to over-estimation. We do not find evidence for a deteriorating productivity; on the contrary productivity improves over time, indicating that estimators in the subject organization did learn.

KW - Function Point Analysis

KW - Software Economics

KW - Software Estimation

UR - http://www.scopus.com/inward/record.url?scp=84974575554&partnerID=8YFLogxK

U2 - 10.1145/2897695.2897698

DO - 10.1145/2897695.2897698

M3 - Conference contribution

SP - 8

EP - 14

BT - WETSoM 2016 Proceedings of the 7th International Workshop on Emerging Trends in Software Metrics,

PB - Association for Computing Machinery (ACM)

CY - New York

ER -

ID: 12627819