FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3048297080> ?p ?o ?g. }

• W3048297080 abstract "Abstract [ Context ]A software vulnerability becomes harmful for software when an attacker successfully exploits the insecure code and reveals the vulnerability. A single vulnerability in code can put the entire software at risk. Therefore, maintaining software security throughout the software life cycle is an important and at the same time challenging task for development teams. This can also leave the door open for vulnerable code being evolved during successive releases. In recent years, researchers have used software metrics‐based vulnerability prediction approaches to detect vulnerable code early and ensure secure code releases. Software metrics have been employed to predict vulnerability specifically in C/C++ and Java‐based systems. However, the prediction performance of metrics at different granularity levels (class level or method level) has not been analyzed. In this paper, we focused on metrics that are specific to lower granularity levels (Java classes and methods). Based on statistical analysis, we first identified a set of class‐level metrics and a set of method‐level metrics and then employed them as features in machine learning techniques to predict vulnerable classes and methods, respectively. This paper describes a comparative study on how our selected metrics perform at different granularity levels. Such a comparative study can help the developers in choosing the appropriate metrics (at the desired level of granularity). [ Objective ] The goal of this research is to propose a set of metrics at two lower granularity levels and provide evidence for their usefulness during vulnerability prediction (which will help in maintaining secure code and ensure secure software evolution). [ Method ] For four Java‐based open source systems (including two releases of Apache Tomcat), we designed and conducted experiments based on statistical tests to propose a set of software metrics that can be used for predicting vulnerable code components (i.e., vulnerable classes and methods). Next, we used our identified metrics as features to train supervised machine learning algorithms to classify Java code as vulnerable or non‐vulnerable. [ Result ] Our study has successfully identified a set of class‐level metrics and a second set of method‐level metrics that can be useful from a vulnerability prediction standpoint. We achieved recall higher than 70% and precision higher than 75% in vulnerability prediction using our identified class‐level metrics as features of machine learning. Furthermore, method‐level metrics showed recall higher than 65% and precision higher than 80%." @default.
• W3048297080 created "2020-08-13" @default.
• W3048297080 creator A5028665931 @default.
• W3048297080 creator A5035247633 @default.
• W3048297080 creator A5043640255 @default.
• W3048297080 date "2020-08-07" @default.
• W3048297080 modified "2023-09-24" @default.
• W3048297080 title "Using software metrics for predicting vulnerable classes and methods in Java projects: A machine learning approach" @default.
• W3048297080 cites W1964962870 @default.
• W3048297080 cites W1978813754 @default.
• W3048297080 cites W1979810153 @default.
• W3048297080 cites W1997236144 @default.
• W3048297080 cites W2004758929 @default.
• W3048297080 cites W2043837581 @default.
• W3048297080 cites W2055765785 @default.
• W3048297080 cites W2056878746 @default.
• W3048297080 cites W2067148378 @default.
• W3048297080 cites W2069205948 @default.
• W3048297080 cites W2079753286 @default.
• W3048297080 cites W2100310618 @default.
• W3048297080 cites W2125343911 @default.
• W3048297080 cites W2137726309 @default.
• W3048297080 cites W2137789775 @default.
• W3048297080 cites W2150866946 @default.
• W3048297080 cites W2154398797 @default.
• W3048297080 cites W2155524176 @default.
• W3048297080 cites W2159613309 @default.
• W3048297080 cites W2166336492 @default.
• W3048297080 cites W2167352226 @default.
• W3048297080 cites W2194432963 @default.
• W3048297080 cites W2244669237 @default.
• W3048297080 cites W2297096600 @default.
• W3048297080 cites W2504360466 @default.
• W3048297080 cites W2508791575 @default.
• W3048297080 cites W2565690877 @default.
• W3048297080 cites W2607665225 @default.
• W3048297080 cites W2612957914 @default.
• W3048297080 cites W2735834919 @default.
• W3048297080 cites W2740329368 @default.
• W3048297080 cites W2748690817 @default.
• W3048297080 cites W2767210280 @default.
• W3048297080 cites W2946283185 @default.
• W3048297080 cites W3101228802 @default.
• W3048297080 cites W4239510810 @default.
• W3048297080 cites W4299689471 @default.
• W3048297080 cites W1983248291 @default.
• W3048297080 doi "https://doi.org/10.1002/smr.2303" @default.
• W3048297080 hasPublicationYear "2020" @default.
• W3048297080 type Work @default.
• W3048297080 sameAs 3048297080 @default.
• W3048297080 citedByCount "14" @default.
• W3048297080 countsByYear W30482970802021 @default.
• W3048297080 countsByYear W30482970802022 @default.
• W3048297080 countsByYear W30482970802023 @default.
• W3048297080 crossrefType "journal-article" @default.
• W3048297080 hasAuthorship W3048297080A5028665931 @default.
• W3048297080 hasAuthorship W3048297080A5035247633 @default.
• W3048297080 hasAuthorship W3048297080A5043640255 @default.
• W3048297080 hasConcept C117447612 @default.
• W3048297080 hasConcept C119857082 @default.
• W3048297080 hasConcept C124101348 @default.
• W3048297080 hasConcept C154945302 @default.
• W3048297080 hasConcept C165696696 @default.
• W3048297080 hasConcept C177264268 @default.
• W3048297080 hasConcept C177774035 @default.
• W3048297080 hasConcept C199360897 @default.
• W3048297080 hasConcept C22680326 @default.
• W3048297080 hasConcept C2776760102 @default.
• W3048297080 hasConcept C2777212361 @default.
• W3048297080 hasConcept C2777904410 @default.
• W3048297080 hasConcept C29983905 @default.
• W3048297080 hasConcept C38652104 @default.
• W3048297080 hasConcept C41008148 @default.
• W3048297080 hasConcept C527648132 @default.
• W3048297080 hasConcept C529173508 @default.
• W3048297080 hasConcept C548217200 @default.
• W3048297080 hasConcept C62913178 @default.
• W3048297080 hasConcept C82214349 @default.
• W3048297080 hasConcept C95713431 @default.
• W3048297080 hasConceptScore W3048297080C117447612 @default.
• W3048297080 hasConceptScore W3048297080C119857082 @default.
• W3048297080 hasConceptScore W3048297080C124101348 @default.
• W3048297080 hasConceptScore W3048297080C154945302 @default.
• W3048297080 hasConceptScore W3048297080C165696696 @default.
• W3048297080 hasConceptScore W3048297080C177264268 @default.
• W3048297080 hasConceptScore W3048297080C177774035 @default.
• W3048297080 hasConceptScore W3048297080C199360897 @default.
• W3048297080 hasConceptScore W3048297080C22680326 @default.
• W3048297080 hasConceptScore W3048297080C2776760102 @default.
• W3048297080 hasConceptScore W3048297080C2777212361 @default.
• W3048297080 hasConceptScore W3048297080C2777904410 @default.
• W3048297080 hasConceptScore W3048297080C29983905 @default.
• W3048297080 hasConceptScore W3048297080C38652104 @default.
• W3048297080 hasConceptScore W3048297080C41008148 @default.
• W3048297080 hasConceptScore W3048297080C527648132 @default.
• W3048297080 hasConceptScore W3048297080C529173508 @default.
• W3048297080 hasConceptScore W3048297080C548217200 @default.
• W3048297080 hasConceptScore W3048297080C62913178 @default.
• W3048297080 hasConceptScore W3048297080C82214349 @default.
• W3048297080 hasConceptScore W3048297080C95713431 @default.

• next