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Krzysztof Czarnecki - One of the best experts on this subject based on the ideXlab platform.
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generating range fixes for software configuration
International Conference on Software Engineering, 2012Co-Authors: Yingfei Xiong, Arnaud Hubaux, Steven She, Krzysztof CzarneckiAbstract:To prevent ill-formed configurations, highly configurable software often allows defining Constraints over the available options. As these Constraints can be complex, fixing a configuration that violates one or more Constraints can be challenging. Although several fix-generation approaches exist, their applicability is limited because (1) they typically generate only one fix, failing to cover the solution that the user wants; and (2) they do not fully support non-Boolean Constraints, which contain arithmetic, inequality, and string operators. This paper proposes a novel concept, range fix, for software configuration. A range fix specifies the options to change and the ranges of values for these options. We also design an algorithm that automatically generates range fixes for a Violated Constraint. We have evaluated our approach with three different strategies for handling Constraint interactions, on data from five open source projects. Our evaluation shows that, even with the most complex strategy, our approach generates complete fix lists that are mostly short and concise, in a fraction of a second.
Yingfei Xiong - One of the best experts on this subject based on the ideXlab platform.
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Generating range fixes for software configuration
2013Co-Authors: Yingfei Xiong, Arnaud Hubaux, Steven SheAbstract:of scholarly and technical work on a non-commercial basis. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and Constraints invoked by each author’s copyright. These works may not be reposted without the explicit permission of the copyright holder. Figure 1: The eCos Configurator Abstract—To prevent configuration errors, highly configurable software often allows defining Constraints over the available options. As these Constraints can be complex, fixing a configuration that violates one or more Constraints can be challenging. Although several fix-generation approaches exist, their applicability is limited because (1) they typically generate only one fix, failing to cover the solution that the user wants; and (2) they do not fully support non-Boolean Constraints, which contain arithmetic, inequality, and string operators. This paper proposes a novel concept, range fix, for software configuration. A range fix specifies the options to change and the range of values at these options. We also design an algorithm that automatically generates range fixes for a Violated Constraint, based on Reiter’s theory of diagnosis. We have evaluated our approach on data from five open source projects, showing that, for our data set, the algorithm generates complete fix lists that are mostly short and concise, in a fraction of a second. I
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generating range fixes for software configuration
International Conference on Software Engineering, 2012Co-Authors: Yingfei Xiong, Arnaud Hubaux, Steven She, Krzysztof CzarneckiAbstract:To prevent ill-formed configurations, highly configurable software often allows defining Constraints over the available options. As these Constraints can be complex, fixing a configuration that violates one or more Constraints can be challenging. Although several fix-generation approaches exist, their applicability is limited because (1) they typically generate only one fix, failing to cover the solution that the user wants; and (2) they do not fully support non-Boolean Constraints, which contain arithmetic, inequality, and string operators. This paper proposes a novel concept, range fix, for software configuration. A range fix specifies the options to change and the ranges of values for these options. We also design an algorithm that automatically generates range fixes for a Violated Constraint. We have evaluated our approach with three different strategies for handling Constraint interactions, on data from five open source projects. Our evaluation shows that, even with the most complex strategy, our approach generates complete fix lists that are mostly short and concise, in a fraction of a second.
Steven She - One of the best experts on this subject based on the ideXlab platform.
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Generating range fixes for software configuration
2013Co-Authors: Yingfei Xiong, Arnaud Hubaux, Steven SheAbstract:of scholarly and technical work on a non-commercial basis. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and Constraints invoked by each author’s copyright. These works may not be reposted without the explicit permission of the copyright holder. Figure 1: The eCos Configurator Abstract—To prevent configuration errors, highly configurable software often allows defining Constraints over the available options. As these Constraints can be complex, fixing a configuration that violates one or more Constraints can be challenging. Although several fix-generation approaches exist, their applicability is limited because (1) they typically generate only one fix, failing to cover the solution that the user wants; and (2) they do not fully support non-Boolean Constraints, which contain arithmetic, inequality, and string operators. This paper proposes a novel concept, range fix, for software configuration. A range fix specifies the options to change and the range of values at these options. We also design an algorithm that automatically generates range fixes for a Violated Constraint, based on Reiter’s theory of diagnosis. We have evaluated our approach on data from five open source projects, showing that, for our data set, the algorithm generates complete fix lists that are mostly short and concise, in a fraction of a second. I
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generating range fixes for software configuration
International Conference on Software Engineering, 2012Co-Authors: Yingfei Xiong, Arnaud Hubaux, Steven She, Krzysztof CzarneckiAbstract:To prevent ill-formed configurations, highly configurable software often allows defining Constraints over the available options. As these Constraints can be complex, fixing a configuration that violates one or more Constraints can be challenging. Although several fix-generation approaches exist, their applicability is limited because (1) they typically generate only one fix, failing to cover the solution that the user wants; and (2) they do not fully support non-Boolean Constraints, which contain arithmetic, inequality, and string operators. This paper proposes a novel concept, range fix, for software configuration. A range fix specifies the options to change and the ranges of values for these options. We also design an algorithm that automatically generates range fixes for a Violated Constraint. We have evaluated our approach with three different strategies for handling Constraint interactions, on data from five open source projects. Our evaluation shows that, even with the most complex strategy, our approach generates complete fix lists that are mostly short and concise, in a fraction of a second.
Arnaud Hubaux - One of the best experts on this subject based on the ideXlab platform.
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Generating range fixes for software configuration
2013Co-Authors: Yingfei Xiong, Arnaud Hubaux, Steven SheAbstract:of scholarly and technical work on a non-commercial basis. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and Constraints invoked by each author’s copyright. These works may not be reposted without the explicit permission of the copyright holder. Figure 1: The eCos Configurator Abstract—To prevent configuration errors, highly configurable software often allows defining Constraints over the available options. As these Constraints can be complex, fixing a configuration that violates one or more Constraints can be challenging. Although several fix-generation approaches exist, their applicability is limited because (1) they typically generate only one fix, failing to cover the solution that the user wants; and (2) they do not fully support non-Boolean Constraints, which contain arithmetic, inequality, and string operators. This paper proposes a novel concept, range fix, for software configuration. A range fix specifies the options to change and the range of values at these options. We also design an algorithm that automatically generates range fixes for a Violated Constraint, based on Reiter’s theory of diagnosis. We have evaluated our approach on data from five open source projects, showing that, for our data set, the algorithm generates complete fix lists that are mostly short and concise, in a fraction of a second. I
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generating range fixes for software configuration
International Conference on Software Engineering, 2012Co-Authors: Yingfei Xiong, Arnaud Hubaux, Steven She, Krzysztof CzarneckiAbstract:To prevent ill-formed configurations, highly configurable software often allows defining Constraints over the available options. As these Constraints can be complex, fixing a configuration that violates one or more Constraints can be challenging. Although several fix-generation approaches exist, their applicability is limited because (1) they typically generate only one fix, failing to cover the solution that the user wants; and (2) they do not fully support non-Boolean Constraints, which contain arithmetic, inequality, and string operators. This paper proposes a novel concept, range fix, for software configuration. A range fix specifies the options to change and the ranges of values for these options. We also design an algorithm that automatically generates range fixes for a Violated Constraint. We have evaluated our approach with three different strategies for handling Constraint interactions, on data from five open source projects. Our evaluation shows that, even with the most complex strategy, our approach generates complete fix lists that are mostly short and concise, in a fraction of a second.
Hoon No - One of the best experts on this subject based on the ideXlab platform.
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a central cutting plane algorithm for convex semi infinite programming problems
Siam Journal on Optimization, 1993Co-Authors: Kenneth O. Kortanek, Hoon NoAbstract:The central cutting plane algorithm for linear semi-infinite programming (SIP) is extended to nonlinear convex SIP of the form min $\{ f ( x )|x \in H,g ( x,t ) \leq 0\,{\text{all}}\,t \in S \}$. Under differentiability assumptions that are weaker than those employed in superlinearly convergent algorithms, a linear convergence rate is established that has additional important features. These features are the ability to (i) generate a cut from any Violated Constraint; (ii) invoke efficient Constraint-dropping rules for management of linear programming (LP) subproblem size; (iii) provide an efficient grid management scheme to generate cuts and ultimately to test feasibility to a high degree of accuracy, as well as to provide an automatic grid refinement for use in obtaining admissible starting solutions for the nonlinear system of first-order conditions; and, (iv) provide primal and dual (Lagrangian) SIP feasible solutions in a finite number of iterations.Numerical tests are provided on a collection of prob...