Predicting feature interactions by using inconsistency models

Crespo, Rui Gustavo

doi:10.1016/j.comnet.2009.09.010

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…Calder et al (2003) showed how programming languages, such as Promela (Calder and Miller, 2001), CSP (Hoare, 1978) and LOTOS (Gorse et al, 2006), may be used to specify features. Methods explored so far in FI detection include simulation (Thomas, 1997), model checking (Plath and Ryan, 2000;Calder and Miller, 2001), theorem proving (Gammelgard and Kristensen, 1994) and prediction (Crespo, 2008).…”

Section: Introductionmentioning

confidence: 99%

Email FI identification and resolution with model checking

Calvinho

Crespo

2011

Journal of Network and Computer Applications

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Section: Introductionmentioning

confidence: 99%

Email FI identification and resolution with model checking

Calvinho

Crespo

2011

Journal of Network and Computer Applications

Self Cite

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“…Otherwise, the feature interaction method was presented in both 3D and feature interaction graph format in a developed software system [9] . G. C. Rui [10] generated hypothetical features for feature interaction detection, which is supported by sets of all possible events, predicates and inconsistent behaviors.…”

Section: Introductionmentioning

confidence: 99%

Extracting Feature Sequences in Software Vulnerabilities Based on Closed Sequential Pattern Mining

Wu¹,

Ma²,

Wang³

2013

JSW

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Feature Extraction is significant for determining security vulnerabilities in software. Mining closed sequential patterns provides complete and condensed information for non-redundant frequent sequences generation. In this paper, we discuss the feature interaction problem and propose an efficient algorithm to extract features in vulnerability sequences. Each closed sequential pattern represents a feature in software vulnerabilities. We explore how to efficiently maintain closed sequential patterns in vulnerability sequences. A compact structure WClosedTree is designed to keep closed sequential patterns, and its nice properties are carefully studied. Two main pruning strategies, backwards super pattern condition and equivalent position information condition, are developed to remove frequent but non-closed sequential patterns in WClosedTree. During the process of maintaining WClosedTree, the weight metric of each feature sequence is calculated to better meet the needs of decision makers. Thus, the proposed algorithm can efficiently extract features from vulnerability sequences. The experimental results show that the proposed algorithm significantly improves the runtime efficiency for mining closed sequential patterns, and the feature interaction framework implements feature extraction in software vulnerabilities.

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Detecting and resolving email feature interactions through constraints

Crespo

2011

IET Softw.

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Predicting feature interactions by using inconsistency models

Cited by 3 publications

References 16 publications

Email FI identification and resolution with model checking

Email FI identification and resolution with model checking

Extracting Feature Sequences in Software Vulnerabilities Based on Closed Sequential Pattern Mining

Detecting and resolving email feature interactions through constraints

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