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Mining software effort data: preliminary analysis of visual studio team system data

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Andrew BegelAuthors Info & Claims

MSR '08: Proceedings of the 2008 international working conference on Mining software repositories

Pages 43 - 46

https://doi.org/10.1145/1370750.1370762

Published: 10 May 2008 Publication History

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Abstract

In the software development process, scheduling and predictability are important components to delivering a product on time and within budget. Effort estimation artifacts offer a rich data set for improving scheduling accuracy and for understanding the development process. Effort estimation data for 55 features in the latest release of Visual Studio Team System (VSTS) were collected and analyzed for trends, patterns, and differences. Statistical analysis shows that actual estimation error was positively correlated with feature size, and that in-process metrics of estimation error were also correlated with the final estimation error. These findings suggest that smaller features can be estimated more accurately, and that in-process estimation error metrics can be provide a quantitative supplement to developer intuition regarding high-risk features during the development process.

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Cited By

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Maddila CUpadrasta SBansal CNagappan NGousios Gvan Deursen A(2023)Nudge: Accelerating Overdue Pull Requests toward CompletionACM Transactions on Software Engineering and Methodology10.1145/354479132:2(1-30)Online publication date: 30-Mar-2023
https://dl.acm.org/doi/10.1145/3544791
Matsubara PSteinmacher IGadelha BConte T(2023)Much more than a prediction: Expert-based software effort estimation as a behavioral actEmpirical Software Engineering10.1007/s10664-023-10332-928:4Online publication date: 5-Jul-2023
https://dl.acm.org/doi/10.1007/s10664-023-10332-9
Matsubara PGadelha BSteinmacher IConte T(2022)SEXTAMTJournal of Systems and Software10.1016/j.jss.2021.111148185:COnline publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1016/j.jss.2021.111148
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Index Terms

Mining software effort data: preliminary analysis of visual studio team system data

Recommendations

Improved measurement of software development effort estimation bias
Highlights
- An effort estimate is the estimate of a point in an effort probability distribution.
Abstract Context
While prior software development effort estimation research has examined the properties of estimation error measures, there has not been much research on the properties of measures of estimation bias.
...
A Current Assessment of Software Development Effort Estimation
ESEM '11: Proceedings of the 2011 International Symposium on Empirical Software Engineering and Measurement

In most cases, software projects exceed their estimated effort. It is often assumed that inaccurate estimates are the main reason for these overruns. Contrarily, our results show that this assumption is not reasonable in many cases. We conducted a ...
A review of studies on expert estimation of software development effort

This paper provides an extensive review of studies related to expert estimation of software development effort. The main goal and contribution of the review is to support the research on expert estimation, e.g., to ease other researcher's search for ...

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Information

Published In

MSR '08: Proceedings of the 2008 international working conference on Mining software repositories

May 2008

162 pages

ISBN:9781605580241

DOI:10.1145/1370750

General Chair:
Ahmed E. Hassan
Queen's University, Canada
,
Program Chairs:
Michele Lanza
University of Lugano, Switzerland
,
Michael W. Godfrey
University of Waterloo, Canada

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 10 May 2008

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ICSE '08

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ICSE '08: International Conference on Software Engineering

May 10 - 11, 2008

Leipzig, Germany

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Cited By

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Maddila CUpadrasta SBansal CNagappan NGousios Gvan Deursen A(2023)Nudge: Accelerating Overdue Pull Requests toward CompletionACM Transactions on Software Engineering and Methodology10.1145/354479132:2(1-30)Online publication date: 30-Mar-2023
Matsubara PSteinmacher IGadelha BConte T(2023)Much more than a prediction: Expert-based software effort estimation as a behavioral actEmpirical Software Engineering10.1007/s10664-023-10332-928:4Online publication date: 5-Jul-2023
Matsubara PGadelha BSteinmacher IConte T(2022)SEXTAMTJournal of Systems and Software10.1016/j.jss.2021.111148185:COnline publication date: 1-Mar-2022
Zhang WPan ZWang Z(2020)Prediction Method of Code Review Time Based on Hidden Markov ModelWeb Information Systems and Applications10.1007/978-3-030-60029-7_15(168-175)Online publication date: 22-Sep-2020
Maddila CBansal CNagappan NDumas MPfahl DApel SRusso A(2019)Predicting pull request completion time: a case study on large scale cloud servicesProceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3338906.3340457(874-882)Online publication date: 12-Aug-2019
Karna HVicković LGotovac S(2018)Application of data mining methods for effort estimation of software projectsSoftware: Practice and Experience10.1002/spe.265149:2(171-191)Online publication date: 23-Oct-2018
Shippey THall TCounsell SBowes DGenero MJedlitschka AJørgensen MScanniello GSampath SCaivano DPort D(2016)So You Need More Method Level Datasets for Your Software Defect Prediction?Proceedings of the 10th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement10.1145/2961111.2962620(1-6)Online publication date: 8-Sep-2016
Karna HGotovac S(2015)Estimating software development effort using Bayesian networks2015 23rd International Conference on Software, Telecommunications and Computer Networks (SoftCOM)10.1109/SOFTCOM.2015.7314091(229-233)Online publication date: Sep-2015
Bird CMenzies TZimmermann T(2015)The Art and Science of Analyzing Software DataundefinedOnline publication date: 15-Sep-2015
Karna HGotovac S(2014)Modeling expert effort estimation of software projects2014 22nd International Conference on Software, Telecommunications and Computer Networks (SoftCOM)10.1109/SOFTCOM.2014.7039106(356-360)Online publication date: Sep-2014
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Improved measurement of software development effort estimation bias

A Current Assessment of Software Development Effort Estimation

A review of studies on expert estimation of software development effort