research-article

An empirical model of technical debt and interest

Authors:

Ariadi Nugroho,

Tobias KuipersAuthors Info & Claims

MTD '11: Proceedings of the 2nd Workshop on Managing Technical Debt

Pages 1 - 8

https://doi.org/10.1145/1985362.1985364

Published: 23 May 2011 Publication History

Abstract

Cunningham introduced the metaphor of technical debt as guidance for software developers that must trade engineering quality against short-term goals.

We revisit the technical debt metaphor, and translate it into terms that can help IT executives better understand their IT investments. An approach is proposed to quantify debts (cost to fix technical quality issues) and interest (extra cost spent on maintenance due to technical quality issues). Our approach is based on an empirical assessment method of software quality developed at the Software Improvement Group (SIG). The core part of the technical debt calculation is constructed on the basis of empirical data of 44 systems that are currently being monitored by SIG.

In a case study, we apply the approach to a real system, and discuss how the results provide useful insights on important questions related to IT investment such as the return on investment (ROI) in software quality improvement.

References

[1]

T. Alves, C. Ypma, and J. Visser. Deriving metric thresholds from benchmark data. In 26th Int. Conf. on Software Maintenance. IEEE, 2010.

Digital Library

[2]

R. Baggen, K. Schill, and J. Visser. Standardized code quality benchmarking for improving software maintainability. In Proc. of the 4th International Workshop on Software Quality and Maintainability (SQM'10), 2010.

[3]

D. Bijlsma. Indicators of issue handling efficiency. Master's thesis, University of Amsterdam, 2010.

[4]

N. Brown, Y. Cai, Y. Guo, R. Kazman, M. Kim, P. Kruchten, E. Lim, A. MacCormack, R. Nord, I. Ozkaya, et al. Managing technical debt in software-reliant systems. In Proceedings of the FSE/SDP workshop on Future of software engineering research, pages 47--52. ACM, 2010.

Digital Library

[5]

CAST. CAST worldwide application software quality study: Summary of key findings, 2010.

[6]

S. Chidamber, D. Darcy, and C. Kemerer. Managerial use of metrics for object-oriented software: An exploratory analysis. Software Engineering, IEEE Transactions on, 24(8):629--639, 2002.

Digital Library

[7]

S. Chin, E. Huddleston, W. Bodwell, and I. Gat. The economics of technical debt. Cutter IT Journal, 23(10):11--15, 2010.

[8]

W. Cunningham. The WyCash portfolio management system. ACM SIGPLAN OOPS Messenger, 4(2):29--30, 1993.

Digital Library

[9]

B. e. Abreu and W. Melo. Evaluating the impact of object-oriented design on software quality. In Software Metrics Symposium, 1996., Proceedings of the 3rd International, pages 90--99. IEEE, 2002.

Digital Library

[10]

S. Eick, T. Graves, A. Karr, J. Marron, and A. Mockus. Does code decay? assessing the evidence from change management data. Software Engineering, IEEE Transactions on, 27(1):1--12, 2002.

Digital Library

[11]

M. Fowler. Technical debt quadrant, 2009.

[12]

R. Harrison, S. Counsell, and R. Nithi. Experimental assessment of the effect of inheritance on the maintainability of object-oriented systems. Journal of Systems and Software, 52(2-3):173--179, 2000.

Digital Library

[13]

I. Heitlager, T. Kuipers, and J. Visser. A practical model for measuring maintainability. In Quality of Information and Communications Technology, 2007. QUATIC 2007. 6th International Conference on the, pages 30--39. IEEE, 2007.

Digital Library

[14]

International Organization for Standardization. ISO/IEC 9126-1: Software engineering - product quality - part 1: Quality model, 2001.

[15]

C. Jones. Backfiring: Converting lines of code to function points. Computer, 28(11):87--88, 2002.

Digital Library

[16]

J. Letouzey and T. Coq. The SQALE Analysis Model An analysis model compliant with the representation condition for assessing the Quality of Software Source Code. VALID, 2010.

Digital Library

[17]

S. McConnell. 10x software development.

[18]

Software Productivity Research, LLC. SPR Programming Languages Table Ver. PLT2007c, December 2007.

Cited By

Kanita N(2024)Modèles explicatifs de la production de la dette technique dans les contextes Agile et DevOps : une revue de la littérature basée sur la méthode BIBGTSystèmes d'information & management10.3917/sim.234.0007Volume 28:4(7-41)Online publication date: 30-Apr-2024
https://doi.org/10.3917/sim.234.0007
Perera JTempero ETu YBlincoe K(2024)A Systematic Mapping Study Exploring Quantification Approaches to Code, Design, and Architecture Technical DebtACM Transactions on Software Engineering and Methodology10.1145/367539333:7(1-44)Online publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1145/3675393
Stochel MBorek TWawrowski MChołda P(2024)Business-driven technical debt management using Continuous Debt Valuation Approach (CoDVA)Information and Software Technology10.1016/j.infsof.2023.107333164:COnline publication date: 10-Jan-2024
https://dl.acm.org/doi/10.1016/j.infsof.2023.107333
Show More Cited By

Index Terms

An empirical model of technical debt and interest
1. Social and professional topics
  1. Professional topics
    1. Management of computing and information systems
      1. Implementation management
        Pricing and resource allocation

Recommendations

Technical debt: towards a crisper definition report on the 4th international workshop on managing technical debt

As the pace of software delivery increases and technology rapidly changes, organizations seek guidance on how to insure the sustainability of their software development effort. Over the past four years running the workshops on Managing Technical Debt, ...
Technical debt in software development: from metaphor to theory report on the third international workshop on managing technical debt

The technical debt metaphor is gaining significant traction in the software development community as a way to understand and communicate issues of intrinsic quality, value, and cost. This is a report on a third workshop on managing technical debt, which ...
An Empirical Analysis of Debt Financing on Firm Investment Behavior: Evidence from China
ISME '10: Proceedings of the 2010 International Conference of Information Science and Management Engineering - Volume 02

This study examines the impacts of debt financing on the firms’ investment decisions by employing the method of the multiple linear regression on the data from 2006-2008 of 60 Chinese real estate listed companies. We obtain three major findings. First, ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

MTD '11: Proceedings of the 2nd Workshop on Managing Technical Debt

May 2011

54 pages

ISBN:9781450305860

DOI:10.1145/1985362

Program Chairs:
Ipek Ozkaya
Software Engineering Institute, USA
,
Philippe Kruchten
University of British Columbia, Canada
,
Rod Nord
Software Engineering Institute, USA
,
Nanette Brown
Software Engineering Institute, USA

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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 May 2011

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

ICSE11

Sponsor:

SIGSOFT

ICSE11: International Conference on Software Engineering

May 23, 2011

HI, Waikiki, Honolulu, USA

Acceptance Rates

Overall Acceptance Rate 40 of 92 submissions, 43%

Upcoming Conference

ICSE 2025

2025 IEEE/ACM 46th International Conference on Software Engineering

April 26 - May 3, 2025

Ottawa , ON , Canada

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

106
Total Citations
View Citations
1,586
Total Downloads

Downloads (Last 12 months)98
Downloads (Last 6 weeks)9

Reflects downloads up to 03 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Kanita N(2024)Modèles explicatifs de la production de la dette technique dans les contextes Agile et DevOps : une revue de la littérature basée sur la méthode BIBGTSystèmes d'information & management10.3917/sim.234.0007Volume 28:4(7-41)Online publication date: 30-Apr-2024
https://doi.org/10.3917/sim.234.0007
Perera JTempero ETu YBlincoe K(2024)A Systematic Mapping Study Exploring Quantification Approaches to Code, Design, and Architecture Technical DebtACM Transactions on Software Engineering and Methodology10.1145/367539333:7(1-44)Online publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1145/3675393
Stochel MBorek TWawrowski MChołda P(2024)Business-driven technical debt management using Continuous Debt Valuation Approach (CoDVA)Information and Software Technology10.1016/j.infsof.2023.107333164:COnline publication date: 10-Jan-2024
https://dl.acm.org/doi/10.1016/j.infsof.2023.107333
Zarrad ABahsoon RManimaran P(2024)Optimizing regression testing with AHP-TOPSIS metric system for effective technical debt evaluationAutomated Software Engineering10.1007/s10515-024-00458-531:2Online publication date: 8-Jul-2024
https://doi.org/10.1007/s10515-024-00458-5
Perera JTempero ETu YBlincoe K(2024)Modelling the quantification of requirements technical debtRequirements Engineering10.1007/s00766-024-00424-329:4(421-458)Online publication date: 6-Jul-2024
https://doi.org/10.1007/s00766-024-00424-3
Slupczynski AAlexander PLichter H(2024)Defining and Evaluating Prudence and Recklessness of Enterprise Architecture DebtEnterprise Information Systems10.1007/978-3-031-64755-0_12(237-261)Online publication date: 26-Jul-2024
https://doi.org/10.1007/978-3-031-64755-0_12
Sas DAvgeriou P(2023)An architectural technical debt index based on machine learning and architectural smellsIEEE Transactions on Software Engineering10.1109/TSE.2023.3286179(1-27)Online publication date: 2023
https://doi.org/10.1109/TSE.2023.3286179
Perera JTempero ETu YBlincoe K(2023)Quantifying Requirements Technical Debt: A Systematic Mapping Study and a Conceptual Model2023 IEEE 31st International Requirements Engineering Conference (RE)10.1109/RE57278.2023.00021(123-133)Online publication date: Sep-2023
https://doi.org/10.1109/RE57278.2023.00021
Wang XSchuster HBorrison RKlöpper B(2023)Technical Debt Management in Industrial ML - State of Practice and Management Model Proposal2023 IEEE 21st International Conference on Industrial Informatics (INDIN)10.1109/INDIN51400.2023.10217843(1-9)Online publication date: 18-Jul-2023
https://doi.org/10.1109/INDIN51400.2023.10217843
Fang HCai YKazman RLefever J(2023)Identifying Anti-Patterns in Distributed Systems With Heterogeneous Dependencies2023 IEEE 20th International Conference on Software Architecture Companion (ICSA-C)10.1109/ICSA-C57050.2023.00035(116-120)Online publication date: Mar-2023
https://doi.org/10.1109/ICSA-C57050.2023.00035
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