Efficient software development practices today pose substantial challenges when defending program... more Efficient software development practices today pose substantial challenges when defending program code from possible threats. The increase in application complexity outpaces traditional vulnerability detection methods, thus putting security breaches and financial losses within more likely reach. Sophisticated algorithms of Generative AI (Gen AI) help developers identify bugs in their source code while solving these programming issues. The security functions of Gen AI-based automation help developers launch protective measures for their applications ahead of development completion to decrease security risks. Gen AI algorithms process large code libraries to find repeated patterns that indicate dangerous code entries. Gen AI models train through analysis of large datasets that combine secure and insecure code, so they develop a skill for spotting buffer overflows and SQL injection points. Because of this capability, the new code review operations benefit from enhanced vulnerability detection speed and higher accuracy. Gen AI offers developers tools to create remedy suggestions demonstrating safe coding skills suitable for current agile development conditions. The industry demands secure software development, so combining Gen AI with vulnerability detection and risk analysis will establish standard operations within upcoming periods. Research efforts should focus on investing in AI model development to make them ready for use across multiple programming languages and frameworks. Real-time Gen AI integration within development platforms enables developers to monitor security issues, thus receiving prompt feedback, encouraging them to create applications focused on security standards. Developers develop applications resistant to security threats in a secure data environment through Gen AI implementation.
Financial institutions have undergone fundamental transformation through machine learning technol... more Financial institutions have undergone fundamental transformation through machine learning technology because they deploy this system for analytical data processing, decision support systems, and risk management processes. Organizations apply their powerful algorithms in machine learning to both accurately detect patterns and automate processes while forecasting market trends for large amounts of data. Machine learning brings fundamental sector modification to financial institutions, enabling them to identify fraudulent activity and create automated trading procedures while taking control of credit resources. The processing of soft data obtained from news and social media sentiments enhances the operational efficiency of forecasting systems alongside decision-making capabilities. Financial institutions obtain new opportunities with ML technologies while these technologies develop innovative solutions and operational improvements that lead to market success. Financial departments implementing machine learning technologies create specific, powerful effects on their regulatory compliance while simultaneously enhancing their risk-based operations. The assessment approaches for risk use historical information analysis with static pattern recognition models, which prove insufficient when dealing with present market fluctuations. Machine learning differs from traditional systems because it uses time-sensitive data analysis to detect ailments and project threats accurately. ML technology analyzes fake activities through abnormal behaviors that differ from conventional patterns. Financial institutions perform ML-based systematic regulatory assessments to uncover abnormal transactions, strengthening their AML and KYC regulatory operations. Such systems decrease operational spending and stabilize financial stability to facilitate better security control. Machine learning implements deliver multiple benefits to financial services, but such benefits generate technical challenges for these institutions. Implementing machine learning in finance encounters numerous challenges caused by privacy-related problems, while unknown operational mechanics promote discriminatory machine behavior. The identified situations produce ethical issues, which create risks for legal complications. Financial organizations need to show total transparency and fairness in their ML systems while they meet all current financial regulations and those that emerge in the future. Financial organizations need reliable data protection systems to maintain their confidential records since they manage large amounts of information. A complete success of machine learning systems requires collaboration between technologists, financial experts, and regulators to address operational challenges that will maximize system benefits. Correct implementation alongside continuous developmental efforts will drive ML-based finance innovation toward its complete effective utilization.
The quick-paced development of Generative Artificial Intelligence (Gen AI) has created new possib... more The quick-paced development of Generative Artificial Intelligence (Gen AI) has created new possibilities for software engineering, which mainly involves automated code analysis. The generation of data dependence graphs (DDGs) from source code needs attention because manual work or rule-based systems usually perform this task. Data dependence graphs are vital because they demonstrate the data movement patterns in programs that facilitate debugging and, optimization and security assessment procedures. The article describes how Gen AI transforms Data Dependence Graphs by applying sophisticated machine learning systems to decode and evaluate code relationships at maximal speed and precision. The automation of this task under Gen AI simultaneously decreases human work while building more scalable and precise software analysis tools. Gen AI enables DDG production by allowing models to process big code repository databases to learn dependency connections and data movement patterns. The models use transformer architectures to interpret complex code structures across various programming languages and frameworks. Next-generation artificial intelligence excels over classic systems since it detects hidden dependencies and addresses unusual circumstances within dynamic programming code. Delivery of this function proves essential when developers work with modern codebases that consist of large heterogeneous evolving systems. The article details how Gen AI platforms create DDGs that are interactive and understandable to developers so they can discover system vulnerabilities and optimize runtime performance. DDG adoption through Gen AI faces obstacles due to requirements for premium training resources, processing power, and user-friendly readabilities from AI output generation. The article lists fine-tuning pre-trained models and implementing knowledge related to particular domains as new solutions to tackle identified problems. Gen AIpowered DDG generation systems promise to disappear the gap between AI and software engineering applications, creating revolutionary changes in developer code interaction methods and software development effectiveness.
Efficient software development practices today pose substantial challenges when defending program... more Efficient software development practices today pose substantial challenges when defending program code from possible threats. The increase in application complexity outpaces traditional vulnerability detection methods, thus putting security breaches and financial losses within more likely reach. Sophisticated algorithms of Generative AI (Gen AI) help developers identify bugs in their source code while solving these programming issues. The security functions of Gen AI-based automation help developers launch protective measures for their applications ahead of development completion to decrease security risks. Gen AI algorithms process large code libraries to find repeated patterns that indicate dangerous code entries. Gen AI models train through analysis of large datasets that combine secure and insecure code, so they develop a skill for spotting buffer overflows and SQL injection points. Because of this capability, the new code review operations benefit from enhanced vulnerability detection speed and higher accuracy. Gen AI offers developers tools to create remedy suggestions demonstrating safe coding skills suitable for current agile development conditions. The industry demands secure software development, so combining Gen AI with vulnerability detection and risk analysis will establish standard operations within upcoming periods. Research efforts should focus on investing in AI model development to make them ready for use across multiple programming languages and frameworks. Real-time Gen AI integration within development platforms enables developers to monitor security issues, thus receiving prompt feedback, encouraging them to create applications focused on security standards. Developers develop applications resistant to security threats in a secure data environment through Gen AI implementation.
Financial institutions have undergone fundamental transformation through machine learning technol... more Financial institutions have undergone fundamental transformation through machine learning technology because they deploy this system for analytical data processing, decision support systems, and risk management processes. Organizations apply their powerful algorithms in machine learning to both accurately detect patterns and automate processes while forecasting market trends for large amounts of data. Machine learning brings fundamental sector modification to financial institutions, enabling them to identify fraudulent activity and create automated trading procedures while taking control of credit resources. The processing of soft data obtained from news and social media sentiments enhances the operational efficiency of forecasting systems alongside decision-making capabilities. Financial institutions obtain new opportunities with ML technologies while these technologies develop innovative solutions and operational improvements that lead to market success. Financial departments implementing machine learning technologies create specific, powerful effects on their regulatory compliance while simultaneously enhancing their risk-based operations. The assessment approaches for risk use historical information analysis with static pattern recognition models, which prove insufficient when dealing with present market fluctuations. Machine learning differs from traditional systems because it uses time-sensitive data analysis to detect ailments and project threats accurately. ML technology analyzes fake activities through abnormal behaviors that differ from conventional patterns. Financial institutions perform ML-based systematic regulatory assessments to uncover abnormal transactions, strengthening their AML and KYC regulatory operations. Such systems decrease operational spending and stabilize financial stability to facilitate better security control. Machine learning implements deliver multiple benefits to financial services, but such benefits generate technical challenges for these institutions. Implementing machine learning in finance encounters numerous challenges caused by privacy-related problems, while unknown operational mechanics promote discriminatory machine behavior. The identified situations produce ethical issues, which create risks for legal complications. Financial organizations need to show total transparency and fairness in their ML systems while they meet all current financial regulations and those that emerge in the future. Financial organizations need reliable data protection systems to maintain their confidential records since they manage large amounts of information. A complete success of machine learning systems requires collaboration between technologists, financial experts, and regulators to address operational challenges that will maximize system benefits. Correct implementation alongside continuous developmental efforts will drive ML-based finance innovation toward its complete effective utilization.
The quick-paced development of Generative Artificial Intelligence (Gen AI) has created new possib... more The quick-paced development of Generative Artificial Intelligence (Gen AI) has created new possibilities for software engineering, which mainly involves automated code analysis. The generation of data dependence graphs (DDGs) from source code needs attention because manual work or rule-based systems usually perform this task. Data dependence graphs are vital because they demonstrate the data movement patterns in programs that facilitate debugging and, optimization and security assessment procedures. The article describes how Gen AI transforms Data Dependence Graphs by applying sophisticated machine learning systems to decode and evaluate code relationships at maximal speed and precision. The automation of this task under Gen AI simultaneously decreases human work while building more scalable and precise software analysis tools. Gen AI enables DDG production by allowing models to process big code repository databases to learn dependency connections and data movement patterns. The models use transformer architectures to interpret complex code structures across various programming languages and frameworks. Next-generation artificial intelligence excels over classic systems since it detects hidden dependencies and addresses unusual circumstances within dynamic programming code. Delivery of this function proves essential when developers work with modern codebases that consist of large heterogeneous evolving systems. The article details how Gen AI platforms create DDGs that are interactive and understandable to developers so they can discover system vulnerabilities and optimize runtime performance. DDG adoption through Gen AI faces obstacles due to requirements for premium training resources, processing power, and user-friendly readabilities from AI output generation. The article lists fine-tuning pre-trained models and implementing knowledge related to particular domains as new solutions to tackle identified problems. Gen AIpowered DDG generation systems promise to disappear the gap between AI and software engineering applications, creating revolutionary changes in developer code interaction methods and software development effectiveness.
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Papers by Shubham Shukla
engineering, which mainly involves automated code analysis. The generation of data dependence graphs (DDGs) from
source code needs attention because manual work or rule-based systems usually perform this task. Data dependence
graphs are vital because they demonstrate the data movement patterns in programs that facilitate debugging and,
optimization and security assessment procedures. The article describes how Gen AI transforms Data Dependence
Graphs by applying sophisticated machine learning systems to decode and evaluate code relationships at maximal
speed and precision. The automation of this task under Gen AI simultaneously decreases human work while building
more scalable and precise software analysis tools.
Gen AI enables DDG production by allowing models to process big code repository databases to learn dependency
connections and data movement patterns. The models use transformer architectures to interpret complex code
structures across various programming languages and frameworks. Next-generation artificial intelligence excels over
classic systems since it detects hidden dependencies and addresses unusual circumstances within dynamic
programming code. Delivery of this function proves essential when developers work with modern codebases that
consist of large heterogeneous evolving systems. The article details how Gen AI platforms create DDGs that are
interactive and understandable to developers so they can discover system vulnerabilities and optimize runtime
performance.
DDG adoption through Gen AI faces obstacles due to requirements for premium training resources, processing power,
and user-friendly readabilities from AI output generation. The article lists fine-tuning pre-trained models and
implementing knowledge related to particular domains as new solutions to tackle identified problems. Gen AIpowered DDG generation systems promise to disappear the gap between AI and software engineering applications,
creating revolutionary changes in developer code interaction methods and software development effectiveness.
engineering, which mainly involves automated code analysis. The generation of data dependence graphs (DDGs) from
source code needs attention because manual work or rule-based systems usually perform this task. Data dependence
graphs are vital because they demonstrate the data movement patterns in programs that facilitate debugging and,
optimization and security assessment procedures. The article describes how Gen AI transforms Data Dependence
Graphs by applying sophisticated machine learning systems to decode and evaluate code relationships at maximal
speed and precision. The automation of this task under Gen AI simultaneously decreases human work while building
more scalable and precise software analysis tools.
Gen AI enables DDG production by allowing models to process big code repository databases to learn dependency
connections and data movement patterns. The models use transformer architectures to interpret complex code
structures across various programming languages and frameworks. Next-generation artificial intelligence excels over
classic systems since it detects hidden dependencies and addresses unusual circumstances within dynamic
programming code. Delivery of this function proves essential when developers work with modern codebases that
consist of large heterogeneous evolving systems. The article details how Gen AI platforms create DDGs that are
interactive and understandable to developers so they can discover system vulnerabilities and optimize runtime
performance.
DDG adoption through Gen AI faces obstacles due to requirements for premium training resources, processing power,
and user-friendly readabilities from AI output generation. The article lists fine-tuning pre-trained models and
implementing knowledge related to particular domains as new solutions to tackle identified problems. Gen AIpowered DDG generation systems promise to disappear the gap between AI and software engineering applications,
creating revolutionary changes in developer code interaction methods and software development effectiveness.