An Empirical Study of a Model for Program Error Prediction
IEEE Transactions on Software Engineering
Function point analysis
Applied software measurement: assuring productivity and quality
Applied software measurement: assuring productivity and quality
Software requirements: objects, functions, and states
Software requirements: objects, functions, and states
Improving the Reliability of Function Point Measurement: An Empirical Study
IEEE Transactions on Software Engineering - Special issue on software measurement principles, techniques, and environments
Quality software management (vol. 2): first-order measurement
Quality software management (vol. 2): first-order measurement
Reliability of function points measurement: a field experiment
Communications of the ACM
Quantitative assessment of the software maintenance process and requirements volatility
CSC '93 Proceedings of the 1993 ACM conference on Computer science
Metrics for requirements engineering
Selected papers of the sixth annual Oregon workshop on Software metrics
Software requirements & specifications: a lexicon of practice, principles and prejudices
Software requirements & specifications: a lexicon of practice, principles and prejudices
The year 2000 software problem: quantifying the costs and assessing the consequences
The year 2000 software problem: quantifying the costs and assessing the consequences
An examination of the effects of requirements changes on software maintenance releases
Journal of Software Maintenance: Research and Practice
Function point analysis: measurement practices for successful software projects
Function point analysis: measurement practices for successful software projects
Software Engineering Economics
Software Engineering Economics
The Mythical Man-Month: Essays on Softw
The Mythical Man-Month: Essays on Softw
Requirements Engineering: A Good Practice Guide
Requirements Engineering: A Good Practice Guide
Requirements Engineering: Processes and Techniques
Requirements Engineering: Processes and Techniques
Exploring Requirements: Quality Before Design
Exploring Requirements: Quality Before Design
DSDM: A Framework for Business-Centered Development
DSDM: A Framework for Business-Centered Development
Quatitative IT portolio management
Science of Computer Programming
Reliability Measurement: From Theory to Practice
IEEE Software
Large-Scale Project Management Is Risk Management
IEEE Software
IEEE Software
A Comparison of Function Point Counting Techniques
IEEE Transactions on Software Engineering
A Study of the Impact of Requirements Volatility on Software Project Performance
APSEC '02 Proceedings of the Ninth Asia-Pacific Software Engineering Conference
Requirements Volatility and Defect Density
ISSRE '99 Proceedings of the 10th International Symposium on Software Reliability Engineering
Software Requirements
Critical Success Factors in Software Maintenance-A Case Study
ICSM '03 Proceedings of the International Conference on Software Maintenance
A study to investigate the impact of requirements instability on software defects
ACM SIGSOFT Software Engineering Notes
Analysis of Requirements Volatility during Software Development Life Cycle
ASWEC '04 Proceedings of the 2004 Australian Software Engineering Conference
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Requirements-Led Project Management: Discovering David's Slingshot
Requirements-Led Project Management: Discovering David's Slingshot
Data warehouse governance: best practices at blue cross and blue shield of North Carolina
Decision Support Systems
Quantifying the value of IT-investments
Science of Computer Programming
An Industrial Case Study on Requirements Volatility Measures
APSEC '05 Proceedings of the 12th Asia-Pacific Software Engineering Conference
Estimating Software Costs
Mastering the Requirements Process (2nd Edition)
Mastering the Requirements Process (2nd Edition)
Quantifying the effects of IT-governance rules
Science of Computer Programming
IEEE Transactions on Software Engineering
IEEE Transactions on Software Engineering
A Light Functional Dimension Estimation Model for Software Maintenance
EQUITY '07 Proceedings of the 2007 IEEE International Conference on Exploring Quantifiable IT Yields
The reengineering of a software system for glaucoma analysis
Computer Methods and Programs in Biomedicine
Computers and Electronics in Agriculture
How to steer an embedded software project: tactics for selecting the software process model
Information and Software Technology
Requirements engineering for organizational transformation
Information and Software Technology
Timeboxing: a process model for iterative software development
Journal of Systems and Software
Quantifying IT estimation risks
Science of Computer Programming
Quantifying IT forecast quality
Science of Computer Programming
Obsolete software requirements
Information and Software Technology
Hi-index | 0.00 |
In an organization operating in the bancassurance sector we identified a low-risk IT subportfolio of 84 IT projects comprising together 16,500 function points, each project varying in size and duration, for which we were able to quantify its requirements volatility. This representative portfolio stems from a much larger portfolio of IT projects. We calculated the volatility from the function point countings that were available to us. These figures were aggregated into a requirements volatility benchmark. We found that maximum requirements volatility rates depend on size and duration, which refutes currently known industrial averages. For instance, a monthly growth rate of 5% is considered a critical failure factor, but in our low-risk portfolio we found more than 21% of successful projects with a volatility larger than 5%. We proposed a mathematical model taking size and duration into account that provides a maximum healthy volatility rate that is more in line with the reality of low-risk IT portfolios. Based on the model, we proposed a tolerance factor expressing the maximal volatility tolerance for a project or portfolio. For a low-risk portfolio its empirically found tolerance is apparently acceptable, and values exceeding this tolerance are used to trigger IT decision makers. We derived two volatility ratios from this model, the @p-ratio and the @r-ratio. These ratios express how close the volatility of a project has approached the danger zone when requirements volatility reaches a critical failure rate. The volatility data of a governmental IT portfolio were juxtaposed to our bancassurance benchmark, immediately exposing a problematic project, which was corroborated by its actual failure. When function points are less common, e.g. in the embedded industry, we used daily source code size measures and illustrated how to govern the volatility of a software product line of a hardware manufacturer. With the three real-world portfolios we illustrated that our results serve the purpose of an early warning system for projects that are bound to fail due to excessive volatility. Moreover, we developed essential requirements volatility metrics that belong on an IT governance dashboard and presented such a volatility dashboard.