Legacy software systems, especially those built on COBOL, are both a marvel and a menace. As of 2023, these systems power 70% of financial institutions, handling $3 trillion in daily transactions--a figure drawn from a comprehensive industry report [1]. Globally, an estimated 200 billion lines of COBOL code remain in use, a testament to its durability since its inception in 1959, as noted in a 2024 IEEE analysis [2]. These systems underpin critical operations--think payroll processing, account management, and loan calculations in banks and insurance firms. Yet, their age is a liability: complexity averages 18 paths per module, well above the modern ideal of 10, while coupling averages 8 dependencies, fostering a 30% higher defect rate than languages like Java [3]. Maintenance consumes 60% of IT budgets, a burden intensified by a dwindling COBOL workforce--now just 5% of developers, per a 2025 Gartner estimate [4]. Modernization is not optional; it is a necessity, and this study proposes an AI-driven solution to transform COBOL into Java, with React illuminating the results.

Recent advances in Large Language Model (LLM) based Generative AI techniques have made it feasible to translate enterprise-level code from legacy languages such as COBOL to modern languages such as Java or Python. While the results of LLM-based automatic transformation are encouraging, the resulting code cannot be trusted to correctly translate the original code, making manual validation of translated Java code from COBOL a necessary but time-consuming and labor-intensive process. In this paper, we share our experience of developing a testing framework for IBM Watsonx Code Assistant for Z (WCA4Z) [5], an industrial tool designed for COBOL to Java translation. The framework automates the process of testing the functional equivalence of the translated Java code against the original COBOL programs in an industry context. Our framework uses symbolic execution to generate unit tests for COBOL, mocking external calls and transforming them into JUnit tests to validate semantic equivalence with translated Java. The results not only help identify and repair any detected discrepancies but also provide feedback to improve the AI model.

Software engineers have joined the ranks of copy editors, translators, and others who fear that they're about to be replaced by generative AI. But it might be surprising to learn that coders have been under threat before. New technologies have long promised to "disrupt" engineering, and these innovations have always failed to get rid of the need for human software developers. If anything, they often made these workers that much more indispensable. To understand where handwringing about the end of programmers comes from--and why it's overblown--we need to look back at the evolution of coding and computing.

ChatGPT as a Python Programming Assistant by Matthew Mayo ChatGPT: Everything You Need to Know by Nisha Arya Explainable AI: 10 Python Libraries for Demystifying Your Model's Decisions by Maryam Miradi How to Use Python and Machine Learning to Predict Football Match Winners by Vaishnavi Amira Yada 20 Questions (with Answers) to Detect Fake Data Scientists: ChatGPT Edition, Part 1 by Matthew Mayo

Make Google Translate From Scratch :JAVASCRIPT, PHP ,AJAX Cour to work with php and javascript in the same time using ajax

Data science courses are among the most popular globally, with a high likelihood of career prospects, according to the volume of internet searches for skill development or job-oriented courses. Data scientists are needed everywhere. The most fundamental prerequisite for developing any technology in this era of smart technology (which includes smartphones, televisions, watches, etc.) is data, and these data scientists serve as the foundation for machine learning and artificial intelligence specialists. A data scientist will also assist organizations in managing serious crises and assisting them in their resolution through the use of data-driven judgments. Data science is the study of analyzing and obtaining organized, unstructured, and noisy data from various sources. This analysis aids businesses in forecasting outcomes and making data-driven decisions. Data that adheres to a data model, has a clearly defined structure, follows a persistent order, and is simple for both humans and programmes to retrieve is said to be structured data. Unstructured data is not structured in a way that has been predefined, notwithstanding the possibility that it has a native, internal structure. The data is kept in its original format; there is no data model. Media, text, internet activity, monitoring photos, and more are typical instances of large datasets. Data Science – The MUST KNOW to become a successful Data Scientist! How can software engineers and data scientists work together? Corrupted data, a type of unstructured data, is another name for noisy data. It also includes any information that a user's system is unable to effectively analyze and interpret. If handled improperly, noisy data can have a negative impact on the outcomes of any data analysis and skew conclusions. Sometimes, statistical analysis is employed to remove noise from noisy data.

This Python Course is for beginners. In this course, you will learn the Python basics through video lectures, quizzes, review exercises, and programming challenges. You will also understand computer science concepts such as flow control and functions. And you will also use Pycharm to write their Python programs.

You will require coding skills if you want to work in the field of artificial intelligence (AI). How do you begin? and Which programming language is ideal for AI? How about starting with these programming languages? I will list high-level overview of the top programming languages for artificial intelligence in this article, along with an explanation of their salient characteristics. In the world of computer science, artificial intelligence is one of the most exciting and dynamic areas. As we speak, it has already begun to change the environment.

If you're new to the topic, the hardest part of mastering machine learning is figuring out where to start. It is normal to question the ideal language for machine learning, regardless of whether you are looking to brush up on your machine learning knowledge or completely change careers. Finding the ideal programming language for machine learning is undoubtedly difficult because over 700 distinct programming languages are widely used, and each has advantages and disadvantages. The good news is that you'll start to identify which programming language will best suit a business problem you are trying to address as you start your journey as a machine learning engineer. Which programming language is ideal for machine learning is certainly on your mind if you're considering a career in this area. While numerous options are available for various uses, in this post, we'll focus on the top machine learning languages. It's crucial to comprehend the fundamentals of creating an ML model before discovering why particular programming languages are better suited for ML.

Nearly two-thirds of large enterprises are running mainframe-based apps dating back two decades, according to the recent Mainframe Modernization Business Barometer Report from Advanced. Over a quarter of businesses run production applications that are as much as 30 years old–some even go back to the 1960s. For example, in a conversation with a friend at a U.S. public pension fund with nearly $100 billion under management, he told me they decided to take action and migrate most of their remaining mainframe applications from COBOL to Java. Well, for one thing, it was hard to find developers who knew the language, or wanted to, with COBOL ranking #1 as the "most dreaded" programming language in Stack Overflow's annual survey. But there were more reasons for embracing Java, starting with a desire to make better use of DevOps to improve software delivery. When migrating from COBOL (or any language) to Java (or any language), it's smart to start with testing requirements.