Lisp and Symbolic Computation
Skyblue: a multi-way local propagation constraint solver for user interface construction
UIST '94 Proceedings of the 7th annual ACM symposium on User interface software and technology
Automated Diagnosis of Product-Line Configuration Errors in Feature Models
SPLC '08 Proceedings of the 2008 12th International Software Product Line Conference
SAT-based analysis of feature models is easy
Proceedings of the 13th International Software Product Line Conference
Variability modeling in the real: a perspective from the operating systems domain
Proceedings of the IEEE/ACM international conference on Automated software engineering
FastXplain: conflict detection for constraint-based recommendation problems
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
A study of non-Boolean constraints in variability models of an embedded operating system
Proceedings of the 15th International Software Product Line Conference, Volume 2
An empirical study on configuration errors in commercial and open source systems
SOSP '11 Proceedings of the Twenty-Third ACM Symposium on Operating Systems Principles
A user survey of configuration challenges in Linux and eCos
Proceedings of the Sixth International Workshop on Variability Modeling of Software-Intensive Systems
Feature models, grammars, and propositional formulas
SPLC'05 Proceedings of the 9th international conference on Software Product Lines
Generating range fixes for software configuration
Proceedings of the 34th International Conference on Software Engineering
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Large modern software systems are often organized as product lines, requiring specialists to configure variability models before delivering a product. Variability models capture both the commonality and variability of different products, and help detect the configurations errors. Existing approaches can recommend fixes for the errors automatically. However, the recommended fixes are sometimes large and complex, and existing approaches lack guidance to help users identify a desirable fix. This paper proposes an approach to provide such guidance using dynamic priorities. The basic idea is to first generate one fix, and then gradually reach the desirable fix based on user feedback. To this end, our approach (1) automatically translates user feedback into a set of implicit priority levels on configuration variables, using five priority assignment and adjustment strategies and (2) efficiently generates potential desirable fixes by calculating new values for the variables with low priority. The experiments on real variability models show that we can reduce up to 89% of the fixes, and up to 98% of the variables shown to the user, compared to when no priorities are used.