Risk analysis in software development

Authors:
Craig Comstock;Zhizhong Jiang;Peter Naudé
Affiliations:
University of Oxford, Oxford, UK;Manchester Business School, Manchester, UK;Manchester Business School, Manchester, UK
Venue:
AIC'08 Proceedings of the 8th conference on Applied informatics and communications
Year:
2008

Citing 14
Cited 0

Improving the Reliability of Function Point Measurement: An Empirical Study

IEEE Transactions on Software Engineering - Special issue on software measurement principles, techniques, and environments
Software engineering: principles and practice

Software engineering: principles and practice
Third and fourth generation language productivity differences

Communications of the ACM
Determinants of software maintenance profiles: an empirical investigation

Journal of Software Maintenance: Research and Practice
Performance Evaluation of General and Company Specific Models in Software Development Effort Estimation

Management Science
Modeling Software Measurement Data

IEEE Transactions on Software Engineering
Software development cost estimation approaches – A survey

Annals of Software Engineering
An Empirical Study of Analogy-based Software Effort Estimation

Empirical Software Engineering
The Impact of Tools on Software Productivity

IEEE Software
Prototyping vs. specifying: A multi-project experiment

ICSE '84 Proceedings of the 7th international conference on Software engineering
Software Engineering: A Practitioner's Approach

Software Engineering: A Practitioner's Approach
Preliminary Data Analysis Methods in Software Estimation

Software Quality Control
An empirical study of the effect of complexity, platform, and program type on software development effort of business applications

Empirical Software Engineering
Modern Applied Statistics with S

Modern Applied Statistics with S

Quantified Score

Hi-index	0.00

Visualization

Abstract

Software is the key factor influencing the success of computer-based systems. However, software is expensive to develop, and there are great potential risks to deliver software without any overrun of the cost. In this study we investigated the amount of risk in terms of the range of possible productivity that can be expected in a portfolio of software development projects. Working on a large database with 4106 software projects, this study revealed that inefficient development teams can spend as many as 13 times of the effort taken by proficient teams for the development. This study further examined the effects of project size and team size on the variance in software development effort. The results showed that to reduce the risks of cost overruns, a small project is better than a large project when the team size is chosen in advance, and small team is preferable to a large team for a project of a fixed size.