Recommendation of Regression Techniques for Software Maintainability Prediction With Multi-Criteria Decision-Making

Kumar, Ajay; Kaur, Kamaldeep

doi:10.1142/s0219622022500638

Cited by 3 publications

(1 citation statement)

References 64 publications

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“…They have addressed these technologies' impact, adoption, and potential to reshape the financial sector, emphasizing their relevance to sustainability, customer experience, and responsible AI adoption. Several studies are valuable for policymakers, businesses, and researchers navigating the complex terrain of FinTech, technology, and sustainability (e.g., Ahmed et al, 2022;Goodell et al, 2021;He et al, 2022;Kumar & Kaur 2023;Mahmud et al, 2023;Martinelli et al, 2020;Najem et al, 2022;Noreen et al, 2023;Shaik, 2023;Weber et al, 2023).…”

Section: Fintech Credit Scoring and Risk Managementmentioning

confidence: 99%

Machine Learning and Artificial Intelligence Method for FinTech Credit Scoring and Risk Management

Roy,

Vasa

2024

International Journal of Business Analytics

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The ever-changing landscape of financial technology has undergone significant changes owing to advancements in machine learning, artificial intelligence, blockchains, and digitalization. These changes have had a profound impact on the provision of financial services, specifically, credit scoring and lending. This study examines the intersection of financial technology, artificial intelligence, machine learning, blockchain, and digitalization in the context of credit services with a focus on credit scoring and lending. This study addressed three main research questions: The research followed a comprehensive methodology, considering factors such as population, intervention, comparison, outcomes, and setting to ensure that collected data aligns with research objectives. The research questions were structured using the PICOS framework, and the PRISMA model was used for the systematic review and study selection. The publications analysed covered a wide range of datasets and methodologies.

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Section: Fintech Credit Scoring and Risk Managementmentioning

confidence: 99%

Machine Learning and Artificial Intelligence Method for FinTech Credit Scoring and Risk Management

Roy,

Vasa

2024

International Journal of Business Analytics

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MCDM-EFS: A novel ensemble feature selection method for software defect prediction using multi-criteria decision making

Kaur,

Kumar

2023

IDT

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Software defect prediction models are used for predicting high risk software components. Feature selection has significant impact on the prediction performance of the software defect prediction models since redundant and unimportant features make the prediction model more difficult to learn. Ensemble feature selection has recently emerged as a new methodology for enhancing feature selection performance. This paper proposes a new multi-criteria-decision-making (MCDM) based ensemble feature selection (EFS) method. This new method is termed as MCDM-EFS. The proposed method, MCDM-EFS, first generates the decision matrix signifying the feature’s importance score with respect to various existing feature selection methods. Next, the decision matrix is used as the input to well-known MCDM method TOPSIS for assigning a final rank to each feature. The proposed approach is validated by an experimental study for predicting software defects using two classifiers K-nearest neighbor (KNN) and naïve bayes (NB) over five open-source datasets. The predictive performance of the proposed approach is compared with existing feature selection algorithms. Two evaluation metrics – nMCC and G-measure are used to compare predictive performance. The experimental results show that the MCDM-EFS significantly improves the predictive performance of software defect prediction models against other feature selection methods in terms of nMCC as well as G-measure.

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Decision Making on a Software Upgrade or Decommission with Data Mining and Machine Learning Techniques in Information Technology Industry

Kowdeed

2024

International Journal of Innovative Science and Research Techno

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The Organizations have been investing more in Technology and Infrastructure spends like software upgrades, software renewals, software replacements, platform migrations etc., apart from investment in Business, People, and Processes. In this context, it is not an easy task for stakeholders to decide whether to go for a software upgrade or to replace it with another software. There is no unified approach or solution to consolidate data and relationships of Information Technology Assets, Software Upgrades, Software costs, Software defects, Software Performance Metrics, Security issues, IT system versions, service level objectives etc. Due to this, the decision making of software upgrades and software decommissioning is a tedious process and takes more time and effort. There is a need to build a solution that can integrate and validate the information like software assets, software upgrade success and failure likelihoods, cost benefit analysis of Cloud Computing, software metrics for fault prediction, software maintainability prediction results, Digital Transformation readiness and other related factors. There is an opportunity to apply Machine Learning techniques in defining and deriving the success likelihoods on the following data: Systems and data integration, software assets compatibility, operational service level agreement breaches, quality assurance metrics, security issues, number of open defects, number of defect fixes, number of priority incidents, mean time to resolve critical incidents, expected cost increase in software maintenance, potential cost reduction with the software or hardware replacement etc. This Research Proposal outlines the above mentioned to build a recommendation system aka decision tree namely Software Upgrades or Decommissions Life Cycle.

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Recommendation of Regression Techniques for Software Maintainability Prediction With Multi-Criteria Decision-Making

Cited by 3 publications

References 64 publications

Machine Learning and Artificial Intelligence Method for FinTech Credit Scoring and Risk Management

Machine Learning and Artificial Intelligence Method for FinTech Credit Scoring and Risk Management

MCDM-EFS: A novel ensemble feature selection method for software defect prediction using multi-criteria decision making

Decision Making on a Software Upgrade or Decommission with Data Mining and Machine Learning Techniques in Information Technology Industry

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