Paper: Tracking Technical Debt- An Exploratory Case Study
Authors: Yuepu Guo, Carolyn Seaman, Rebeka Gomes, Antonio Cavalcanti, Graziela Tonin, Fabio Q. B. Da Silva, André L. M. Santos, Clauirton Siebra
Session: Early Research Achievement Track Session 3
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ERA - Tracking Technical Debt
1. ICSM 2011
Williamsburg,VA, USA
May 25 – May 30, 2011
Tracking Technical Debt
— An Exploratory Case Study
Yuepu Guo, Carolyn Seaman
Information Systems Department, University of Maryland Baltimore County, USA
Rebeka Gomes, Antonio Cavalcanti, Graziela Tonin, Fabio
Q. B. Da Silva, André L. M. Santos, Clauirton Siebra
CIn/UFPE - Center of Informatics at Federal University of Pernambuco, Brazil
2. Introduction
Technical Debt
Delaying software maintenance tasks for short term gain but possible long term cost
Risk – additional cost (interest) and uncertainty of interest payment
Decision – what technical debt should be paid off and when?
Management Practice
IMPLICIT technical debt management
Experience-based decision making (no rigorous measurement)
Proposed Research
Cost-benefit relationships of incurring technical debt
Costs and benefits of EXPLICIT technical debt management
Retrospective study
Research Questions
How and to what extent technical debt affects software projects?
Is the decision simulation is effective to uncover the benefits of explicit technical debt
management?
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3. Background
Larger Research Project
Measuring and Monitoring Technical Debt
Technical Debt Management Framework
ID 20
Date TD
7/18/2009
Measurement
Responsible Rose Angel
Type TD Documentation TD
Location Identification Module S Monitoring
Description In the last release, function F was added to
TD
module S, but the documentation has not
been updated to reflect this change.
Principal List
3.5 person-day
Interest Amount: 1.5 person-day
Interest Probability 40%
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4. Subject Technical Debt Item
System
Software application from a multi-national company
Client solution for Microsoft Exchange Server
63,218 LOC and over 5 years of evolution with 20 developers involved
D1 D3
Evolution D2
R1 R2
Cost X
2011
D1: Decision to maintain WebDAV communication protocol
D1: Decision to maintain WebDAV protocol
D2: Decision to couple persistenceand communication layers
D2: Decision to coupple persistence
and communication layers
D3: Decision to support MS Exchange 2007
D3: Decision to upgrade to ActiveSync communication protocol
R1: First system release
R2: First maintenance release
R1: First system release
R2: Second system release
Technical Debt Item
Delayed change of the communication protocol (T1)
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5. Methodology
Measurement
Technical debt item (T1)
Principal: Effort to switch to ActiveSync (new protocol)
Interest Amount: Rework effort on affected modules
Interest Probability
Coupling the communication and persistence layers
Switching to ActiveSync
Impact of T1
Gain: Principal
Loss: Interest Amount × Interest Probability
Decision Simulation Process
Estimate
Track TD item Simulate Compare
principal and
at D1 decision decisions
interest
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6. Results
Decisions D1 D2 D3
Actual
Defer Defer Defer
Decision
Simulated
Pay Pay Pay
Decision
Simulated Cost/Benefit Analysis The value of explicit
technical debt management
1000 929
800
Labor-hour
585
600 507 507 507
400
Beneift
Cost
200
65
0
D1 D2 D3
6 Decision Point
7. Conclusion
Technical debt may have significant impact on software
projects (tripled the development cost in this case)
A decision made without careful analysis could
aggravate the negative effect of technical debt
Explicit management of technical debt could prevent
high cost incurred by technical debt
Decision simulation provides an effective approach to
the technical debt problem
Business factors
Real benefit of incurring the technical debt
Over-simplicity of the approach
Multi-disciplinary team
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