@article {17798,
	title = {Optimal models of disjunctive logic programs: semantics, complexity, and computation},
	journal = {Knowledge and Data Engineering, IEEE Transactions on},
	volume = {16},
	year = {2004},
	month = {2004/04//},
	pages = {487 - 503},
	abstract = {Almost all semantics for logic programs with negation identify a set, SEM(P), of models of program P, as the intended semantics of P, and any model M in this class is considered a possible meaning of P with regard to the semantics the user has in mind. Thus, for example, in the case of stable models [M. Gelfond et al., (1988)], choice models [D. Sacca et al., (1990)], answer sets [M. Gelfond et al., (1991)], etc., different possible models correspond to different ways of "completing" the incomplete information in the logic program. However, different end-users may have different ideas on which of these different models in SEM(P) is a reasonable one from their point of view. For instance, given SEM(P), user U<sub>1</sub> may prefer model M<sub>1</sub> isin;SEM(P) to model M<sub>2</sub> isin;SEM(P) based on some evaluation criterion that she has. We develop a logic program semantics based on optimal models. This semantics does not add yet another semantics to the logic programming arena - it takes as input an existing semantics SEM(P) and a user-specified objective function Obj, and yields a new semantics Opt(P)_ sube; SEM(P) that realizes the objective function within the framework of preferred models identified already by SEM(P). Thus, the user who may or may not know anything about logic programming has considerable flexibility in making the system reflect her own objectives by building "on top" of existing semantics known to the system. In addition to the declarative semantics, we provide a complete complexity analysis and algorithms to compute optimal models under varied conditions when SEM(P) is the stable model semantics, the minimal models semantics, and the all-models semantics.},
	keywords = {complexity;, computational, disjunctive, function;, knowledge, LANGUAGE, Logic, minimal, model, nonmonotonic, objective, optimisation;, OPTIMIZATION, problems;, program, Programming, programming;, reasoning;, representation;, semantics;, stable, user-specified},
	isbn = {1041-4347},
	doi = {10.1109/TKDE.2004.1269672},
	author = {Leone,N. and Scarcello,F. and V.S. Subrahmanian}
}
@conference {18400,
	title = {Maintaining software with a security perspective},
	booktitle = {Software Maintenance, 2002. Proceedings. International Conference on},
	year = {2002},
	month = {2002///},
	pages = {194 - 203},
	abstract = {Testing for software security is a lengthy, complex and costly process. Currently, security testing is done using penetration analysis and formal verification of security kernels. These methods are not complete and are difficult to use. Hence it is essential to focus testing effort in areas that have a greater number of security vulnerabilities to develop secure software as well as meet budget and time constraints. We propose a testing strategy based on a classification of vulnerabilities to develop secure and stable systems. This taxonomy will enable a system testing and maintenance group to understand the distribution of security vulnerabilities and prioritize their testing effort according to the impact the vulnerabilities have on the system. This is based on Landwehr{\textquoteright}s (1994) classification scheme for security flaws and we evaluated it using a database of 1360 operating system vulnerabilities. This analysis indicates vulnerabilities tend to be focused in relatively few areas and associated with a small number of software engineering issues.},
	keywords = {(computers);, budget, classification, classification;, constraints;, data;, engineering;, flaw, maintenance;, of, operating, program, scheme;, Security, software, stable, system, systems, systems;, testing;, TIME, vulnerabilities;, vulnerability},
	doi = {10.1109/ICSM.2002.1167766},
	author = {Jiwnani,K. and Zelkowitz, Marvin V}
}
@article {17727,
	title = {On the stability of sequential updates and downdates},
	journal = {Signal Processing, IEEE Transactions on},
	volume = {43},
	year = {1995},
	month = {1995/11//},
	pages = {2642 - 2648},
	abstract = {The updating and downdating of Cholesky decompositions has important applications in a number of areas. There is essentially one standard updating algorithm, based on plane rotations, which is backward stable. Three downdating algorithms have been treated in the literature: the LINPACK algorithm, the method of hyperbolic transformations, and Chambers{\textquoteright} (1971) algorithm. Although none of these algorithms is backward stable, the first and third satisfy a relational stability condition. It is shown that relational stability extends to a sequence of updates and downdates. In consequence, other things being equal, if the final decomposition in the sequence is well conditioned, it will be accurately computed, even though intermediate decompositions may be almost completely inaccurate. These results are also applied to the two-sided orthogonal decompositions, such as the URV decomposition},
	keywords = {algorithm;Cholesky, algorithm;downdating, algorithm;error, algorithm;URV, algorithms;hyperbolic, analysis;matrix, analysis;sequential, Chambers{\textquoteright}, condition;rounding, decomposition;backward, decomposition;numerical, decomposition;plane, decompositions;LINPACK, decompositions;updating, downdates;sequential, error, errors;sequences;, orthogonal, rotations;relational, stability, stability;roundoff, stable, transformations;matrix, updates;stability;two-sided},
	isbn = {1053-587X},
	doi = {10.1109/78.482114},
	author = {Stewart, G.W.}
}
