An empirical validation of software cost estimation models
Communications of the ACM
STATEMATE: A Working Environment for the Development of Complex Reactive Systems
IEEE Transactions on Software Engineering
Software project dynamics: an integrated approach
Software project dynamics: an integrated approach
Experimental results on the application of satisfiability algorithms to scheduling problems
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
COBRA: a hybrid method for software cost estimation, benchmarking, and risk assessment
Proceedings of the 20th international conference on Software engineering
Little-JIL/Juliette: a process definition language and interpreter
Proceedings of the 22nd international conference on Software engineering
Software Engineering Economics
Software Engineering Economics
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Software Cost Estimation with Cocomo II with Cdrom
Software Cost Estimation with Cocomo II with Cdrom
Safe and Simple Software Cost Analysis
IEEE Software
A Knowledge-Based Environment for Modeling and Simulating Software Engineering Processes
IEEE Transactions on Knowledge and Data Engineering
Using Little-JIL to Coordinate Agents in Software Engineering
ASE '00 Proceedings of the 15th IEEE international conference on Automated software engineering
Simulation with Arena
Selecting Best Practices for Effort Estimation
IEEE Transactions on Software Engineering
The business case for automated software engineering
Proceedings of the twenty-second IEEE/ACM international conference on Automated software engineering
Backdoors to typical case complexity
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Can we build software faster and better and cheaper?
PROMISE '09 Proceedings of the 5th International Conference on Predictor Models in Software Engineering
On the Relative Merits of Software Reuse
ICSP '09 Proceedings of the International Conference on Software Process: Trustworthy Software Development Processes
Case-based reasoning vs parametric models for software quality optimization
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
A second look at Faster, Better, Cheaper
Innovations in Systems and Software Engineering
Regularities in learning defect predictors
PROFES'10 Proceedings of the 11th international conference on Product-Focused Software Process Improvement
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Most process models calibrate their internal settings using historicaldata. Collecting this data is expensive, tedious, and often an incomplete process. Is it possible to make accurate software process estimates without historicaldata? Suppose much of uncertainty in a model comes from a small subset of themodel variables. If so, then after (a) ranking variables by their ability to constrainthe output; and (b) applying a small number of the top-ranked variables; then itshould be possible to (c) make stable predictions in the constrained space. To test that hypothesis, we combined a simulated annealer (to generate randomsolutions) with a variable ranker. The results where quite dramatic: in one ofthe studies in this paper, we found process options that reduced the median andvariance of the effort estimates by a factor of 20. In ten case studies, we show thatthe estimates generated in this manner are usually similar to those produced bystandard local calibration. Our conclusion is that while it is always preferable to tune models to localdata, it is possible to learn process control options without that data.