Article

How to avoid drastic software process change (using stochastic stability)

Authors:

Steve Williams,

Jairus HihnAuthors Info & Claims

ICSE '09: Proceedings of the 31st International Conference on Software Engineering

Pages 540 - 550

https://doi.org/10.1109/ICSE.2009.5070552

Published: 16 May 2009 Publication History

Abstract

Before performing drastic changes to a project, it is worthwhile to thoroughly explore the available options within the current structure of a project. An alternative to drastic change are internal changes that adjust current options within a software project. In this paper, we show that the effects of numerous internal changes can out-weigh the effects of drastic changes. That is, the benefits of drastic change can often be achieved without disrupting a project.

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Cited By

Nair VAgrawal AChen JFu WMathew GMenzies TMinku LWagner MYu ZZaidman AKamei YHill E(2018)Data-driven search-based software engineeringProceedings of the 15th International Conference on Mining Software Repositories10.1145/3196398.3196442(341-352)Online publication date: 28-May-2018
Arcuri ABriand L(2014)A Hitchhiker's guide to statistical tests for assessing randomized algorithms in software engineeringSoftware Testing, Verification & Reliability10.1002/stvr.148624:3(219-250)Online publication date: 1-May-2014
Menzies TKocaguneli ETurhan BMinku LPeters F(2014)Sharing Data and Models in Software EngineeringundefinedOnline publication date: 22-Dec-2014
Show More Cited By

Index Terms

How to avoid drastic software process change (using stochastic stability)
1. Social and professional topics
  1. Professional topics
    1. Management of computing and information systems
      1. Software management
2. Software and its engineering
  1. Software creation and management
    1. Software post-development issues
      1. Software reverse engineering

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Information

Published In

cover image ACM Conferences

ICSE '09: Proceedings of the 31st International Conference on Software Engineering

May 2009

643 pages

ISBN:9781424434534

Sponsors

SIGSOFT: ACM Special Interest Group on Software Engineering

Publisher

IEEE Computer Society

United States

Publication History

Published: 16 May 2009

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Overall Acceptance Rate 276 of 1,856 submissions, 15%

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Cited By

Nair VAgrawal AChen JFu WMathew GMenzies TMinku LWagner MYu ZZaidman AKamei YHill E(2018)Data-driven search-based software engineeringProceedings of the 15th International Conference on Mining Software Repositories10.1145/3196398.3196442(341-352)Online publication date: 28-May-2018
Arcuri ABriand L(2014)A Hitchhiker's guide to statistical tests for assessing randomized algorithms in software engineeringSoftware Testing, Verification & Reliability10.1002/stvr.148624:3(219-250)Online publication date: 1-May-2014
Menzies TKocaguneli ETurhan BMinku LPeters F(2014)Sharing Data and Models in Software EngineeringundefinedOnline publication date: 22-Dec-2014
Menzies TTurhan B(2013)Beyond data mining; towards "idea engineering"Proceedings of the 9th International Conference on Predictive Models in Software Engineering10.1145/2499393.2499401(1-6)Online publication date: 9-Oct-2013
Arcuri ABriand LTaylor RGall HMedvidović N(2011)A practical guide for using statistical tests to assess randomized algorithms in software engineeringProceedings of the 33rd International Conference on Software Engineering10.1145/1985793.1985795(1-10)Online publication date: 21-May-2011
Brady AMenzies TMenzies TKoru G(2010)Case-based reasoning vs parametric models for software quality optimizationProceedings of the 6th International Conference on Predictive Models in Software Engineering10.1145/1868328.1868333(1-10)Online publication date: 12-Sep-2010
Menzies TEl-Rawas OHihn JBoehm BOstrand T(2009)Can we build software faster and better and cheaper?Proceedings of the 5th International Conference on Predictor Models in Software Engineering10.1145/1540438.1540442(1-9)Online publication date: 18-May-2009

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