The Effect of Weighted Moving Windows on Security Vulnerability Prediction

Kudjo, Patrick Kwaku; Chen, Jinfu; Brown, Selasie Aformaley; Mensah, Solomon

doi:10.1109/asew.2019.00031

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…We argue, supported by our empirical analyses, that MCMC modeling based on the introduced step‐by‐step Bellwether method can be used to leverage the BMW resulting in relatively higher prediction accuracy for new project cases. A generalization of the finding of a significant effect of using moving windows remains an open challenge since a variety of chronological datasets,* modeling approaches and accuracy measures have been considered in previous studies 18,23–27 . In some cases, improved prediction accuracy was not achieved 26 ; in two of our previous studies, 15,17 it was found that the BMWs sampled (from the ISBSG, Desharnais, and Kitchenham datasets) did result in improved prediction accuracy.…”

Section: Introductionmentioning

confidence: 92%

“…A generalization of the finding of a significant effect of using moving windows remains an open challenge since a variety of chronological datasets,* modeling approaches and accuracy measures have been considered in previous studies. 18,[23][24][25][26][27] In some cases, improved prediction accuracy was not achieved 26 ; in two of our previous studies, 15,17 it was found that the BMWs sampled (from the ISBSG, Desharnais, and Kitchenham datasets) did result in improved prediction accuracy. Although previous study 15 made use of three datasets (ISBSG-projects in the Communication sector, Desharnais and Kitchenham), it only used a single learner, namely, deep neural networks (DNN) to predict the software effort.…”

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confidence: 91%

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A classification scheme to improve conclusion instability using Bellwether moving windows

Mensah

Kudjo

2022

J Software Evolu Process

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Context The use of a subset of recently completed and exemplary data, namely, Bellwether moving window (BMW) has proven successful to result in improved accuracy in software effort estimation (SEE). These outcomes were achieved based on the theory that estimation outcome of a future event depends on previous events. Thus, the existence of a BMW yield improved prediction accuracy for new project estimation. However, the conclusion instability problem across learners still threatens the reliability of SEE for new projects. Such instability concerns are attributed to the data subset considered for the training and validation needs of learners. Objective To investigate whether the use of BMWs together with an effort classification scheme can minimize the conclusion instability problem across learners. Method We apply a Bellwether method comprising of three operators, namely, SORT+CLUSTER, GENERATE_TPM, and APPLY to sample the BMW from a pool of chronological projects from the Maxwell and International Software Benchmarking Standards Group (ISBSG) datasets. The sampled BMW is benchmarked against the entire collection of preprocessed projects, namely, growing portfolio to evaluate prediction and classification accuracy across a set of learners–ElasticNet regression, deep neural networks, and automatically transformed linear model. Results (1) BMW exists in the studied projects and (2) training the learners with a BMW of average window size 28.5%–75.5% of the growing portfolio (not older than 3 years) relatively minimizes the conclusion instability of prediction results. Conclusion When BMWs are available, we recommend their use for estimating the effort for a new project to minimize the conclusion instability problem.

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Section: Introductionmentioning

confidence: 92%

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confidence: 91%

A classification scheme to improve conclusion instability using Bellwether moving windows

Mensah

Kudjo

2022

J Software Evolu Process

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Modeling Software Vulnerability Injection-Discovery Process Incorporating Time-Delay and VIKOR Based Ranking

Agarwal

Aggrawal

Das

et al. 2022

Springer Series in Reliability Engineering

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Automated Risk Management Based Software Security Vulnerabilities Management

et al. 2022

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An automated risk assessment approach is explored in this work. The focus is to optimize the conventional threat modeling approach to explore software system vulnerabilities. Data produced in the software development processes are better leveraged using Machine Learning approaches. A large amount of industry knowledge around security vulnerabilities can be leveraged to enhance current threat modeling approaches. Work done here is in the ecosystem of software development processes that use Agile methodology. Insurance business domain data are explored as a target for this study. The focus is to enhance the traditional threat modeling approach with a better quantitative approach and reduce the biases introduced by the people who are part of software development processes. This effort will help bridge multiple data sources prevalent across the software development ecosystem. Bringing these various data sources together will assist in understanding patterns associated with security aspects of the software systems. This perspective further helps to understand and devise better controls. Approaches explored so far have considered individual areas of software development and their influence on improving security. There is a need to build an integrated approach for a total security solution for the software systems. A wide variety of machine learning approaches and ensemble approaches will be explored. The insurance business domain is considered for the research here. CWE (Common Weaknesses Enumeration) mapping from industry knowledge are leveraged to validate the security needs from the industry perspective. This combination of industry and company data will help get a holistic picture of the software system's security. Combining the industry and company data helps lay down the path for an integrated security management system in software development. The risk management framework with the quantitative threat modeling process is the work's uniqueness. This work contributes towards making the software systems secure and robust with time.

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The Effect of Weighted Moving Windows on Security Vulnerability Prediction

Cited by 3 publications

References 18 publications

A classification scheme to improve conclusion instability using Bellwether moving windows

A classification scheme to improve conclusion instability using Bellwether moving windows

Modeling Software Vulnerability Injection-Discovery Process Incorporating Time-Delay and VIKOR Based Ranking

Automated Risk Management Based Software Security Vulnerabilities Management

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