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research-article

Software Effort, Quality, and Cycle Time: A Study of CMM Level 5 Projects

Authors:

Manish Agrawal,

Kaushal ChariAuthors Info & Claims

IEEE Transactions on Software Engineering, Volume 33, Issue 3

Pages 145 - 156

https://doi.org/10.1109/TSE.2007.29

Published: 01 March 2007 Publication History

Abstract

The Capability Maturity Model (CMM) has become a popular methodology for improving software development processes with the goal of developing high-quality software within budget and planned cycle time. Prior research literature, while not exclusively focusing on CMM level 5 projects, has identified a host of factors as determinants of software development effort, quality, and cycle time. In this study, we focus exclusively on CMM level 5 projects from multiple organizations to study the impacts of highly mature processes on effort, quality, and cycle time. Using a linear regression model based on data collected from 37 CMM level 5 projects of four organizations, we find that high levels of process maturity, as indicated by CMM level 5 rating, reduce the effects of most factors that were previously believed to impact software development effort, quality, and cycle time. The only factor found to be significant in determining effort, cycle time, and quality was software size. On the average, the developed models predicted effort and cycle time around 12 percent and defects to about 49 percent of the actuals, across organizations. Overall, the results in this paper indicate that some of the biggest rewards from high levels of process maturity come from the reduction in variance of software development outcomes that were caused by factors other than software size.

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Cited By

Graf-Vlachy LWagner S(2024)Cleaning Up Confounding: Accounting for Endogeneity Using Instrumental Variables and Two-Stage ModelsACM Transactions on Software Engineering and Methodology10.1145/367473033:8(1-31)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3674730
Kula EGreuter Evan Deursen AGousios GChandra SBlincoe KTonella P(2023)Dynamic Prediction of Delays in Software Projects using Delay Patterns and Bayesian ModelingProceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3611643.3616328(1012-1023)Online publication date: 30-Nov-2023
https://dl.acm.org/doi/10.1145/3611643.3616328
Sakhrawi ZSellami ABouassida N(2021)Software Enhancement Effort Prediction Using Machine-Learning Techniques: A Systematic Mapping StudySN Computer Science10.1007/s42979-021-00872-62:6Online publication date: 22-Sep-2021
https://dl.acm.org/doi/10.1007/s42979-021-00872-6
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Index Terms

Software Effort, Quality, and Cycle Time: A Study of CMM Level 5 Projects
1. Social and professional topics
  1. Professional topics
    1. Management of computing and information systems
      1. Implementation management
        Pricing and resource allocation
      2. Project and people management
2. Software and its engineering
  1. Software creation and management
    1. Software development process management

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cover image IEEE Transactions on Software Engineering

IEEE Transactions on Software Engineering Volume 33, Issue 3

March 2007

64 pages

ISSN:0098-5589

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Copyright © 2007.

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IEEE Press

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Published: 01 March 2007

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Cited By

Graf-Vlachy LWagner S(2024)Cleaning Up Confounding: Accounting for Endogeneity Using Instrumental Variables and Two-Stage ModelsACM Transactions on Software Engineering and Methodology10.1145/367473033:8(1-31)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3674730
Kula EGreuter Evan Deursen AGousios GChandra SBlincoe KTonella P(2023)Dynamic Prediction of Delays in Software Projects using Delay Patterns and Bayesian ModelingProceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3611643.3616328(1012-1023)Online publication date: 30-Nov-2023
https://dl.acm.org/doi/10.1145/3611643.3616328
Sakhrawi ZSellami ABouassida N(2021)Software Enhancement Effort Prediction Using Machine-Learning Techniques: A Systematic Mapping StudySN Computer Science10.1007/s42979-021-00872-62:6Online publication date: 22-Sep-2021
https://dl.acm.org/doi/10.1007/s42979-021-00872-6
Verma VTandon AAggarwal A(2021)The Moderating Effect of Management Review in Enhancing Software Reliability: A Partial Least Square ApproachInformation Systems Frontiers10.1007/s10796-021-10209-624:6(1845-1863)Online publication date: 3-Oct-2021
https://dl.acm.org/doi/10.1007/s10796-021-10209-6
Ghanbari HVartiainen TSiponen M(2018)Omission of Quality Software Development PracticesACM Computing Surveys10.1145/317774651:2(1-27)Online publication date: 13-Feb-2018
https://dl.acm.org/doi/10.1145/3177746
Chari KAgrawal M(2018)Impact of incorrect and new requirements on waterfall software project outcomesEmpirical Software Engineering10.1007/s10664-017-9506-423:1(165-185)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1007/s10664-017-9506-4
(2017)Research patterns and trends in software effort estimationInformation and Software Technology10.1016/j.infsof.2017.06.00291:C(1-21)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1016/j.infsof.2017.06.002
Parthasarathy SSharma S(2017)Impact of customization over software quality in ERP projectsSoftware Quality Journal10.1007/s11219-016-9314-x25:2(581-598)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1007/s11219-016-9314-x
Chevers DDuggan EMoore S(2017)The Contribution of Process, People and Perception to Information Systems Quality and SuccessThe Electronic Journal of Information Systems in Developing Countries10.1002/j.1681-4835.2012.tb00392.x55:1(1-22)Online publication date: 5-Dec-2017
Ramasubbu NKemerer C(2016)Technical Debt and the Reliability of Enterprise Software SystemsManagement Science10.1287/mnsc.2015.219662:5(1487-1510)Online publication date: 1-May-2016
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