An empirical validation of software cost estimation models
Communications of the ACM
Calibrating estimation tools for software development
Software Engineering Journal
Reformulating and calibrating COCOMO
Journal of Systems and Software
Empirical studies of assumptions that underlie software cost-estimation models
Information and Software Technology
Timid choices and bold forecasts: a cognitive perspective on risk taking
Management Science
Effort estimation using analogy
Proceedings of the 18th international conference on Software engineering
Estimating Software Project Effort Using Analogies
IEEE Transactions on Software Engineering
An evaluation of the paired comparisons method for software sizing
Proceedings of the 22nd international conference on Software engineering
An investigation of machine learning based prediction systems
Journal of Systems and Software - Special issue on empirical studies of software development and evolution
Software Engineering Economics
Software Engineering Economics
On Building Prediction Systems for Software Engineers
Empirical Software Engineering
Improving Subjective Estimates Using Paired Comparisons
IEEE Software
Using Public Domain Metrics To Estimate Software Development Effort
METRICS '01 Proceedings of the 7th International Symposium on Software Metrics
Controlling Software Projects: Management, Measurement, and Estimates
Controlling Software Projects: Management, Measurement, and Estimates
Computational intelligence as an emerging paradigm of software engineering
SEKE '02 Proceedings of the 14th international conference on Software engineering and knowledge engineering
Reliability and Validity in Comparative Studies of Software Prediction Models
IEEE Transactions on Software Engineering
An analysis of data sets used to train and validate cost prediction systems
PROMISE '05 Proceedings of the 2005 workshop on Predictor models in software engineering
A Framework for Design Tradeoffs
Software Quality Control
Using industry based data sets in software engineering research
Proceedings of the 2006 international workshop on Summit on software engineering education
An influence model for factors in outsourced software maintenance: Research Articles
Journal of Software Maintenance and Evolution: Research and Practice
Journal of Systems and Software
Mining software repositories for comprehensible software fault prediction models
Journal of Systems and Software
Journal of Systems and Software
Sizing user stories using paired comparisons
Information and Software Technology
Software development productivity on a new platform: an industrial case study
Information and Software Technology
Integrate the GM(1,1) and Verhulst models to predict software stage effort
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
NLDB'10 Proceedings of the Natural language processing and information systems, and 15th international conference on Applications of natural language to information systems
Evaluation of three methods to predict project success: a case study
PROFES'05 Proceedings of the 6th international conference on Product Focused Software Process Improvement
Expert Systems with Applications: An International Journal
Size doesn't matter?: on the value of software size features for effort estimation
Proceedings of the 8th International Conference on Predictive Models in Software Engineering
Automated trendline generation for accurate software effort estimation
Proceedings of the 3rd annual conference on Systems, programming, and applications: software for humanity
Approximation of COSMIC functional size to support early effort estimation in Agile
Data & Knowledge Engineering
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It is well-known that effective prediction of project cost related factors is an important aspect of software engineering. Unfortunately, despite extensive research over more than 30 years, this remains a significant problem for many practitioners. A major obstacle is the absence of reliable and systematic historic data, yet this is a sine qua non for almost all proposed methods: statistical, machine learning or calibration of existing models. In this paper, we describe our sparse data method (SDM) based upon a pairwise comparison technique and Saaty's Analytic Hierarchy Process (AHP). Our minimum data requirement is a single known point. The technique is supported by a software tool known as DataSalvage. We show, for data from two companies, how our approach驴based upon expert judgement驴adds value to expert judgement by producing significantly more accurate and less biased results. A sensitivity analysis shows that our approach is robust to pairwise comparison errors. We then describe the results of a small usability trial with a practicing project manager. From this empirical work, we conclude that the technique is promising and may help overcome some of the present barriers to effective project prediction.