Software engineering metrics and models
Software engineering metrics and models
Leveraging the new infrastructure: how market leaders capitalize on information technology
Leveraging the new infrastructure: how market leaders capitalize on information technology
Predictors of online buying behavior
Communications of the ACM
Function point analysis: measurement practices for successful software projects
Function point analysis: measurement practices for successful software projects
The Mythical Man-Month: Essays on Softw
The Mythical Man-Month: Essays on Softw
Accuracy of software quality models over multiple releases
Annals of Software Engineering
Quatitative IT portolio management
Science of Computer Programming
Capability Maturity Model, Version 1.1
IEEE Software
Investigation of Logistic Regression as a Discriminant of Software Quality
METRICS '01 Proceedings of the 7th International Symposium on Software Metrics
Quantitative aspects of outsourcing deals
Science of Computer Programming
Quantifying the value of IT-investments
Science of Computer Programming
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Information and Software Technology
Quantifying the effects of IT-governance rules
Science of Computer Programming
Identifying Software Project Risks: An International Delphi Study
Journal of Management Information Systems
IEEE Transactions on Software Engineering
Quantifying the yield of risk-bearing IT-portfolios
Science of Computer Programming
Quantifying requirements volatility effects
Science of Computer Programming
Review: Neural networks and statistical techniques: A review of applications
Expert Systems with Applications: An International Journal
Quantifying IT forecast quality
Science of Computer Programming
Quantifying forecast quality of IT business value
Science of Computer Programming
PROFES'12 Proceedings of the 13th international conference on Product-Focused Software Process Improvement
Investments in information systems: A contribution towards sustainability
Information Systems Frontiers
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A statistical method is proposed for quantifying the impact of factors that influence the quality of the estimation of costs for IT-enabled business projects. We call these factors risk drivers as they influence the risk of the misestimation of project costs. The method can effortlessly be transposed for usage on other important IT key performance indicators (KPIs), such as schedule misestimation or functionality underdelivery. We used logistic regression as a modeling technique to estimate the quantitative impact of risk factors. We did so because logistic regression has been applied successfully in fields including medical science, e.g. in perinatal epidemiology, to answer questions that show a striking resemblance to the questions regarding project risk management. In our study we used data from a large organization in the financial services industry to assess the applicability of logistic modeling in quantifying IT risks. With this real-world example we illustrated how to scrutinize the quality and plausibility of the available data. We explained how to deal with factors that cannot be influenced, also called risk factors, by project management before or in the early stage of a project, but can have an influence on the outcome of the estimation process. We demonstrated how to select the risk drivers using logistic regression. Our research has shown that it is possible to properly quantify these risks, even with the help of crude data. We discussed the interpretation of the models found and showed that the findings are helpful in decision making on measures to be taken to identify potential misestimates and thus mitigate IT risks for individual projects. We proposed increasing the auditing process efficiency by using the found cost misestimation models to classify all projects as either risky projects or non-risky projects. We discovered through our analyses that projects must not be overstaffed and the ratio of external developers must be kept small to obtain better cost estimates. Our research showed that business units that report on financial information tend to be risk mitigating, because they have more cost underruns in comparison with business units without reporting; the latter have more cost overruns. We also discovered a maturity mismatch: an increase from CMM level 1 to 2 did not influence the disparity between a cost estimate and its actual if the maturity of the business is not also increased.