Software defect prediction plays an important role in software quality assurance.However,the performance of the prediction model is susceptible to the irrelevant and redundant features.In addition,previous studies mos...Software defect prediction plays an important role in software quality assurance.However,the performance of the prediction model is susceptible to the irrelevant and redundant features.In addition,previous studies mostly regard software defect prediction as a single objective optimization problem,and multi-objective software defect prediction has not been thoroughly investigated.For the above two reasons,we propose the following solutions in this paper:(1)we leverage an advanced deep neural network-Stacked Contractive AutoEncoder(SCAE)to extract the robust deep semantic features from the original defect features,which has stronger discrimination capacity for different classes(defective or non-defective).(2)we propose a novel multi-objective defect prediction model named SMONGE that utilizes the Multi-Objective NSGAII algorithm to optimize the advanced neural network-Extreme learning machine(ELM)based on state-of-the-art Pareto optimal solutions according to the features extracted by SCAE.We mainly consider two objectives.One objective is to maximize the performance of ELM,which refers to the benefit of the SMONGE model.Another objective is to minimize the output weight norm of ELM,which is related to the cost of the SMONGE model.We compare the SCAE with six state-of-the-art feature extraction methods and compare the SMONGE model with multiple baseline models that contain four classic defect predictors and the MONGE model without SCAE across 20 open source software projects.The experimental results verify that the superiority of SCAE and SMONGE on seven evaluation metrics.展开更多
In today’s rapidly evolving digital landscape,web application security has become paramount as organizations face increasingly sophisticated cyber threats.This work presents a comprehensive methodology for implementi...In today’s rapidly evolving digital landscape,web application security has become paramount as organizations face increasingly sophisticated cyber threats.This work presents a comprehensive methodology for implementing robust security measures in modern web applications and the proof of the Methodology applied to Vue.js,Spring Boot,and MySQL architecture.The proposed approach addresses critical security challenges through a multi-layered framework that encompasses essential security dimensions including multi-factor authentication,fine-grained authorization controls,sophisticated session management,data confidentiality and integrity protection,secure logging mechanisms,comprehensive error handling,high availability strategies,advanced input validation,and security headers implementation.Significant contributions are made to the field of web application security.First,a detailed catalogue of security requirements specifically tailored to protect web applications against contemporary threats,backed by rigorous analysis and industry best practices.Second,the methodology is validated through a carefully designed proof-of-concept implementation in a controlled environment,demonstrating the practical effectiveness of the security measures.The validation process employs cutting-edge static and dynamic analysis tools for comprehensive dependency validation and vulnerability detection,ensuring robust security coverage.The validation results confirm the prevention and avoidance of security vulnerabilities of the methodology.A key innovation of this work is the seamless integration of DevSecOps practices throughout the secure Software Development Life Cycle(SSDLC),creating a security-first mindset from initial design to deployment.By combining proactive secure coding practices with defensive security approaches,a framework is established that not only strengthens application security but also fosters a culture of security awareness within development teams.This hybrid approach ensures that security considerations are woven into every aspect of the development process,rather than being treated as an afterthought.展开更多
[Zn(o-bda)(phen)(H2O)]·H2O (C22H20N2O6Zn) (1) [where o-bda is o-phenylenediacetic acid dianion and phen is 1,10-phenanthroline] crystallizes in triclinic system, space group P1 with a=0.826 5(4) nm, b=1.042 4(5) ...[Zn(o-bda)(phen)(H2O)]·H2O (C22H20N2O6Zn) (1) [where o-bda is o-phenylenediacetic acid dianion and phen is 1,10-phenanthroline] crystallizes in triclinic system, space group P1 with a=0.826 5(4) nm, b=1.042 4(5) nm, c=1.238 1(6) nm, α=76.987(9)°, β=70.987(9)°, γ=78.281(8)°, V=0.9728(8) nm3, Z=2, Dc=1.617 g·cm-3, μ=1.308 mm-1 and F(000)=488. Zn(Ⅱ)ion has a distorted trigonal bipyramid coordination geometry formed by two carboxyl O atoms from two different o-bda groups, two N atoms from the phen ligand and one terminal water molecule. Adjacent Zn(Ⅱ) ions are interlinked by o-bda groups into a infinite zigzag chain structure with a Zn...Zn distance of 0.825 6(4) nm. The adjacent zigzag chains may also be paired under direction of supramolecular recognition and attraction through both π-π stacking and hydrogen bonding interactions into molecular zippers, which further interlinked into a three-dimensional supramolecular network by these noncovalent interactions. CCDC: 600935.展开更多
基金This work is supported in part by the National Science Foundation of China(Grant Nos.61672392,61373038)in part by the National Key Research and Development Program of China(Grant No.2016YFC1202204).
文摘Software defect prediction plays an important role in software quality assurance.However,the performance of the prediction model is susceptible to the irrelevant and redundant features.In addition,previous studies mostly regard software defect prediction as a single objective optimization problem,and multi-objective software defect prediction has not been thoroughly investigated.For the above two reasons,we propose the following solutions in this paper:(1)we leverage an advanced deep neural network-Stacked Contractive AutoEncoder(SCAE)to extract the robust deep semantic features from the original defect features,which has stronger discrimination capacity for different classes(defective or non-defective).(2)we propose a novel multi-objective defect prediction model named SMONGE that utilizes the Multi-Objective NSGAII algorithm to optimize the advanced neural network-Extreme learning machine(ELM)based on state-of-the-art Pareto optimal solutions according to the features extracted by SCAE.We mainly consider two objectives.One objective is to maximize the performance of ELM,which refers to the benefit of the SMONGE model.Another objective is to minimize the output weight norm of ELM,which is related to the cost of the SMONGE model.We compare the SCAE with six state-of-the-art feature extraction methods and compare the SMONGE model with multiple baseline models that contain four classic defect predictors and the MONGE model without SCAE across 20 open source software projects.The experimental results verify that the superiority of SCAE and SMONGE on seven evaluation metrics.
文摘In today’s rapidly evolving digital landscape,web application security has become paramount as organizations face increasingly sophisticated cyber threats.This work presents a comprehensive methodology for implementing robust security measures in modern web applications and the proof of the Methodology applied to Vue.js,Spring Boot,and MySQL architecture.The proposed approach addresses critical security challenges through a multi-layered framework that encompasses essential security dimensions including multi-factor authentication,fine-grained authorization controls,sophisticated session management,data confidentiality and integrity protection,secure logging mechanisms,comprehensive error handling,high availability strategies,advanced input validation,and security headers implementation.Significant contributions are made to the field of web application security.First,a detailed catalogue of security requirements specifically tailored to protect web applications against contemporary threats,backed by rigorous analysis and industry best practices.Second,the methodology is validated through a carefully designed proof-of-concept implementation in a controlled environment,demonstrating the practical effectiveness of the security measures.The validation process employs cutting-edge static and dynamic analysis tools for comprehensive dependency validation and vulnerability detection,ensuring robust security coverage.The validation results confirm the prevention and avoidance of security vulnerabilities of the methodology.A key innovation of this work is the seamless integration of DevSecOps practices throughout the secure Software Development Life Cycle(SSDLC),creating a security-first mindset from initial design to deployment.By combining proactive secure coding practices with defensive security approaches,a framework is established that not only strengthens application security but also fosters a culture of security awareness within development teams.This hybrid approach ensures that security considerations are woven into every aspect of the development process,rather than being treated as an afterthought.
文摘[Zn(o-bda)(phen)(H2O)]·H2O (C22H20N2O6Zn) (1) [where o-bda is o-phenylenediacetic acid dianion and phen is 1,10-phenanthroline] crystallizes in triclinic system, space group P1 with a=0.826 5(4) nm, b=1.042 4(5) nm, c=1.238 1(6) nm, α=76.987(9)°, β=70.987(9)°, γ=78.281(8)°, V=0.9728(8) nm3, Z=2, Dc=1.617 g·cm-3, μ=1.308 mm-1 and F(000)=488. Zn(Ⅱ)ion has a distorted trigonal bipyramid coordination geometry formed by two carboxyl O atoms from two different o-bda groups, two N atoms from the phen ligand and one terminal water molecule. Adjacent Zn(Ⅱ) ions are interlinked by o-bda groups into a infinite zigzag chain structure with a Zn...Zn distance of 0.825 6(4) nm. The adjacent zigzag chains may also be paired under direction of supramolecular recognition and attraction through both π-π stacking and hydrogen bonding interactions into molecular zippers, which further interlinked into a three-dimensional supramolecular network by these noncovalent interactions. CCDC: 600935.
