research-article

An analysis of trends in productivity and cost drivers over years

Authors:

Barry BoehmAuthors Info & Claims

Promise '11: Proceedings of the 7th International Conference on Predictive Models in Software Engineering

Article No.: 3, Pages 1 - 10

https://doi.org/10.1145/2020390.2020393

Published: 20 September 2011 Publication History

Abstract

Background: Software engineering practices have evolved considerably over the last four decades, changing the way software systems are developed and delivered. Such evolvement may result in improvements in software productivity and changes in factors that affect productivity.

Aims: This paper reports our empirical analysis on how changes in software engineering practices are reflected in COCOMO cost drivers and how software productivity has evolved over the years.

Method: The analysis is based on the COCOMO data set of 341 software projects developed between 1970 and 2009. We analyze the productivity trends over the years, comparing productivity of different types and countries. To explain the overall impact of cost drivers on productivity and explain its trends, we propose a measure named Difficulty which is based on the COCOMO model and its cost drivers.

Results: The results of our analysis indicate that the overall productivity of the projects in the data set has increased noticeably over the last 40 years. Our analysis also shows that the productivity trends and productivity variability can be explained by using the proposed Difficulty measure.

Conclusions: Our analysis provides empirical evidence that the productivity trends can be characterized by the improvements in software tools, processes, and platforms among other factors. The Difficulty measure can be used to justify and compare productivity among projects of different characteristics, e.g., different domains, platforms, complexity, and personnel experience. Although we define the measure using the COCOMO cost drivers, it may not fully represent the most important factors influencing productivity. One direction for our future work is to analyze the effectiveness of the measure using more cost drivers on more data points.

References

[1]

B. W. Boehm, "Software Engineering Economics," Prentice Hall, 1981.

Digital Library

[2]

B. W. Boehm, E. Horowitz, R. Madachy, D. Reifer, B. K. Clark, B. Steece, A. W. Brown, S. Chulani, and C. Abts, "Software Cost Estimation with COCOMO II," Prentice Hall, 2000.

Digital Library

[3]

B. W. Boehm, Improving Software Productivity, Computer, Vol. 20, Issue 9, Sept 1987, pp. 43--57

Digital Library

[4]

B. W. Boehm, "A View of 20^th and 21^st Software Engineering," The 28th International Conference on Software Engineering (ICSE), 2006.

Digital Library

[5]

B. W. Boehm, B. Clark, E. Horowitz, C. Westland, R. Madachy, R. Selby, "Cost models for future software life cycle processes: COCOMO 2.0, Annals of Software Engineering 1, Dec., 1995, pp. 57--94.

[6]

L. Briand, I. Wieczorek, "Resource Estimation in Software Engineering," Encyclopedia of Software Engineering, Pp. 1160--1196, Wiley-Interscience Publishing, 2001

[7]

C. Jones, "Estimating Software Cost," McGraw-Hill, New York, NY, 1998.

Digital Library

[8]

A. Trendowicz, J. Munch, "Factors Influencing Software Development Productivity--- State-of-the-Art and Industrial Experiences", Advances in Computers, Vol. 77, 2009, pp. 185--241,

[9]

W. Scacchi, "Understanding Software Productivity", Advances in Software Engineering and Knowledge Engineering, Vol. 4, 1995. Page 37--70.

[10]

D. Reifer, B. W. Boehm, S. Chulani, "The Rosetta Stone: Making COCOMO Estimates Work with COCOMO II," Crosstalk, The Journal of Defense Engineering, Feb, 1999

[11]

R. Kauffman and R. Kumar, "Modeling Estimation Expertise in Object Based ICASE Environments," Stern School of Business Report, New York University, January, 1993

[12]

R. Banker, R. Kauffman, R. Kumar, "An Empirical Test of Object-Based Output Measurement Metrics in a Computer Aided Software Engineering (CASE) Environment," Journal of Management Information System, 1994

Digital Library

[13]

S. Chulani, B. W. Boehm, B. Steece, "Bayesian analysis of empirical software engineering cost models," IEEE Transactions on Software Engineering, vol. 25 n. 4, pp. 573--583, July/August, 1999.

Digital Library

[14]

A. Trendowicz, M. Ochs, A. Wickenkamp, J. Münch, Y. Ishigai, T. Kawaguchi, "An Integrated Approach for Identifying Relevant Factors Influencing Software Development Productivity," Lecture Notes in Computer Science, 2008, pp. 223--237

Digital Library

[15]

C. Jones, "Applied Software Measurement", McGraw-Hill, New York, NY, 2008

[16]

A. De Lucia, E. Pompella, and S. Stefanucci, "Assessing effort estimation models for corrective maintenance through empirical studies", Information and Software Technology 47, 2005, pp. 3--15

[17]

M. Jorgensen, "Experience with the accuracy of software maintenance task effort prediction models", IEEE Transactions on Software Engineering 21 (8), 1995, pp. 674--681.

Digital Library

[18]

M. Jorgensen and M. Shepperd, "A Systematic Review of Software Development Cost Estimation Studies," IEEE Transactions on Software Engineering, v. 33 n. 1, 2007, p. 33--53

[19]

K. D. Maxwell, P. Forselius, "Benchmarking Software-Development Productivity", IEEE Software, Issue 17, Jan 2000, pp. 80--88

Digital Library

[20]

Z. Chen, T. Menzies, D. Port, B. W. Boehm, "Finding the right data for software cost modeling," IEEE Software, Nov 2005, pp. 38--46.

Digital Library

[21]

T. Menzies, Z. Chen, J. Hihn, K. Lum, "Selecting Best Practices for Effort Estimation," IEEE Transactions on Software Engineering, 2006

Digital Library

[22]

V. Nguyen, S. Deeds-Rubin, T. Tan, B. W. Boehm, "A SLOC Counting Standard," COOCOMO II Int'l Forum, 2007. DOI = http://csse.usc.edu/csse/TECHRPTS/2007/usc-csse-2007-737/usc-csse-2007-737.pdf

[23]

R. Premraj, M. Shepperd, B. Kitchenham, P. Forselius, "An Empirical Analysis of Software Productivity over Time," metrics, pp. 37, 11th IEEE International Software Metrics Symposium (METRICS'05), 2005

Digital Library

[24]

S. Chulani, B. Boehm, and B. Steece, "Bayesian analysis of empirical software engineering cost models," IEEE Transactions on Software Engineering, vol. 25 n. 4, pp. 573--583, July/August 1999.

Digital Library

Cited By

Smite DTkalich AMoe NPapatheocharous EKlotins EBuvik M(2022)Changes in perceived productivity of software engineers during COVID-19 pandemicJournal of Systems and Software10.1016/j.jss.2021.111197186:COnline publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1016/j.jss.2021.111197
Bao LLi TXia XZhu KLi HYang X(2022)How does working from home affect developer productivity? — A case study of Baidu during the COVID-19 pandemicScience China Information Sciences10.1007/s11432-020-3278-465:4Online publication date: 14-Mar-2022
https://doi.org/10.1007/s11432-020-3278-4
Uddin GAlam OSerebrenik A(2022)A qualitative study of developers’ discussions of their problems and joys during the early COVID-19 monthsEmpirical Software Engineering10.1007/s10664-022-10156-z27:5Online publication date: 4-Jun-2022
https://dl.acm.org/doi/10.1007/s10664-022-10156-z
Show More Cited By

Index Terms

An analysis of trends in productivity and cost drivers over years
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

Recommendations

Towards an evidence-based theoretical framework on factors influencing the software development productivity
AbstractContext:
Productivity refers to the rate at which a company produces goods, and its observation takes into account the number of people and the amount of other necessary resources to deliver such goods. However, it is not clear how to observe ...
Productivity analysis of Japanese enterprise software development projects
MSR '06: Proceedings of the 2006 international workshop on Mining software repositories

To clarify the relation between controllable attributes of a software development and its productivity, this paper experimentally analyzed a software project repository (SEC repository), consisting of 253 enterprise software development projects in ...
An Empirical Analysis of Software Productivity over Time
METRICS '05: Proceedings of the 11th IEEE International Software Metrics Symposium

OBJECTIVE - the aim is to investigate how software project productivity has changed over time. Within this overall goal we also compare productivity between different business sectors and seek to identify major drivers. METHOD - we analysed a data set ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

Promise '11: Proceedings of the 7th International Conference on Predictive Models in Software Engineering

September 2011

145 pages

ISBN:9781450307093

DOI:10.1145/2020390

General Chair:
Tim Menzies
WVU

Copyright © 2011 ACM.

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 September 2011

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

Promise '11

Promise '11: 7th International Conference on Predictive Models in Software Engineering

September 20 - 21, 2011

Alberta, Banff, Canada

Acceptance Rates

Promise '11 Paper Acceptance Rate 15 of 35 submissions, 43%;

Overall Acceptance Rate 98 of 213 submissions, 46%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

15
Total Citations
View Citations
430
Total Downloads

Downloads (Last 12 months)11
Downloads (Last 6 weeks)1

Reflects downloads up to 02 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Smite DTkalich AMoe NPapatheocharous EKlotins EBuvik M(2022)Changes in perceived productivity of software engineers during COVID-19 pandemicJournal of Systems and Software10.1016/j.jss.2021.111197186:COnline publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1016/j.jss.2021.111197
Bao LLi TXia XZhu KLi HYang X(2022)How does working from home affect developer productivity? — A case study of Baidu during the COVID-19 pandemicScience China Information Sciences10.1007/s11432-020-3278-465:4Online publication date: 14-Mar-2022
https://doi.org/10.1007/s11432-020-3278-4
Uddin GAlam OSerebrenik A(2022)A qualitative study of developers’ discussions of their problems and joys during the early COVID-19 monthsEmpirical Software Engineering10.1007/s10664-022-10156-z27:5Online publication date: 4-Jun-2022
https://dl.acm.org/doi/10.1007/s10664-022-10156-z
Hasan KChakraborty PShahriyar RIqbal AUddin GLanubile F(2021)A Survey-Based Qualitative Study to Characterize Expectations of Software Developers from Five StakeholdersProceedings of the 15th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)10.1145/3475716.3475787(1-11)Online publication date: 11-Oct-2021
https://dl.acm.org/doi/10.1145/3475716.3475787
de Carvalho Almeida WJuristo N(2021)A model using agile methodologies for defining metrics to be used by the public sector in Brazil to set remuneration for outsourced software developmentProceedings of the 43rd International Conference on Software Engineering: Companion Proceedings10.1109/ICSE-Companion52605.2021.00125(272-274)Online publication date: 25-May-2021
https://dl.acm.org/doi/10.1109/ICSE-Companion52605.2021.00125
Shrikanth NNichols WFahid FMenzies T(2021)Assessing practitioner beliefs about software engineeringEmpirical Software Engineering10.1007/s10664-021-09957-526:4Online publication date: 1-Jul-2021
https://dl.acm.org/doi/10.1007/s10664-021-09957-5
Chapetta WTravassos G(2020)Towards an evidence-based theoretical framework on factors influencing the software development productivityEmpirical Software Engineering10.1007/s10664-020-09844-525:5(3501-3543)Online publication date: 1-Sep-2020
https://dl.acm.org/doi/10.1007/s10664-020-09844-5
Nguyen VHuynh TBoehm BHuang LTruong T(2019)Investigating the use of duration‐based windows and estimation by analogy for COCOMOJournal of Software: Evolution and Process10.1002/smr.217631:10Online publication date: 25-Oct-2019
https://dl.acm.org/doi/10.1002/smr.2176
Meyer ABarton LMurphy GZimmermann TFritz T(2017)The Work Life of DevelopersIEEE Transactions on Software Engineering10.1109/TSE.2017.265688643:12(1178-1193)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1109/TSE.2017.2656886
C. Duarte C(2017)Productivity paradoxes revisitedEmpirical Software Engineering10.1007/s10664-016-9453-522:2(818-847)Online publication date: 1-Apr-2017
https://dl.acm.org/doi/10.1007/s10664-016-9453-5
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten