Artificial Intelligence
Knowledge compilation and theory approximation
Journal of the ACM (JACM)
The sciences of the artificial (3rd ed.)
The sciences of the artificial (3rd ed.)
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Detecting change in categorical data: mining contrast sets
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Converging on the Optimal Attainment of Requirements
RE '02 Proceedings of the 10th Anniversary IEEE Joint International Conference on Requirements Engineering
Mining Association Rules with Weighted Items
IDEAS '98 Proceedings of the 1998 International Symposium on Database Engineering & Applications
Data Mining for Very Busy People
Computer
Software Factories: Assembling Applications with Patterns, Models, Frameworks, and Tools
Software Factories: Assembling Applications with Patterns, Models, Frameworks, and Tools
Model-driven development: the good, the bad, and the ugly
IBM Systems Journal - Model-driven software development
A rational approach to model-driven development
IBM Systems Journal - Model-driven software development
Just enough learning (of association rules): the TAR2 "Treatment" learner
Artificial Intelligence Review
Journal of Artificial Intelligence Research
Backdoors to typical case complexity
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
How to avoid drastic software process change (using stochastic stability)
ICSE '09 Proceedings of the 31st International Conference on Software Engineering
Practical challenges of requirements prioritization based on risk estimation
Empirical Software Engineering
Finding robust solutions in requirements models
Automated Software Engineering
Today/future importance analysis
Proceedings of the 12th annual conference on Genetic and evolutionary computation
The relationship between search based software engineering and predictive modeling
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
A baseline method for search-based software engineering
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
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
Reasoning with optional and preferred requirements
ER'10 Proceedings of the 29th international conference on Conceptual modeling
Information and Software Technology
Search-based software engineering: Trends, techniques and applications
ACM Computing Surveys (CSUR)
Exact scalable sensitivity analysis for the next release problem
ACM Transactions on Software Engineering and Methodology (TOSEM)
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Recent work with NASA's Jet Propulsion Laboratory has allowed for external access to five of JPL's real-world requirements models, anonymized to conceal proprietary information, but retaining their computational nature. Experimentation with these models, reported herein, demonstrates a dramatic speedup in the computations performed on them. These models have a well defined goal: select mitigations that retire risks which, in turn, increases the number of attainable requirements. Such a non-linear optimization is a well-studied problem. However identification of not only (a)~the optimal solution(s) but also (b)~the key factors leading to them is less well studied. Our technique, called KEYS, shows a rapid way of simultaneously identifying the solutions and their key factors. KEYS improves on prior work by several orders of magnitude. Prior experiments with simulated annealing or treatment learning took tens of minutes to hours to terminate. KEYS runs much faster than that; e.g for one model, KEYS ran 13,000 times faster than treatment learning (40 minutes versus 0.18 seconds). Processing these JPL models is a non-linear optimization problem: the fewest mitigations must be selected while achieving the most requirements. Non-linear optimization is a well studied problem. With this paper, we challenge other members of the PROMISE community to improve on our results with other techniques.