The manufacturing industry is rapidly changing, creating a growing demand for more intelligent and adaptive systems. With recent developments in artificial intelligence, especially with the onset of large language models (LLMs)Large Language Models (LLMs) such as ChatGPT, new opportunities have emerged for companies to increase their productivity and maximize revenue. In a competitive environment, businesses must constantly innovate to stay ahead. To support innovative and competitive organizations, LLMs can analyze large amounts of data to identify trends and optimize processes. In addition, the industry faces a labor shortage, particularly in roles that require specialized skills. LLMs can fill this gap by providing real-time assistance and training. This knowledge transfer could help less experienced workers perform their tasks more effectively. Regulatory compliance is increasingly imperative in manufacturing, and LLMs can help ensure adherence to safetySafety in manufacturing standards and regulatory requirements. LLMs can address these and other challenges by using their capabilities in data processing, natural language understanding, and predictive analytics. In this chapter, we explain the fundamental concepts behind LLM techniques and how to use them in a smart manufacturing environment such as Industry X.0Industry X.0. We discuss the challenges and future trends of LLMs in different industrial fields. We also highlight the need for LLM frameworks that can guarantee data privacy, security, and ethical usage.

Concept drift (CD) refers to a phenomenon where the data distribution within datasets changes over time, and this can have adverse effects on the performance of prediction models in software engineering (SE), including those used for tasks like cost estimation and defect prediction. Detecting CD in SE datasets is difficult, but important, because it identifies the need for retraining prediction models and in turn improves their performance. If the concept drift is caused by symmetric changes in the data distribution, the model adaptation process might need to account for this symmetry to maintain accurate predictions. This paper explores the impact of CD within the context of cross-version defect prediction (CVDP), aiming to enhance the reliability of prediction performance and to make the data more symmetric. A concept drift detection (CDD) approach is further proposed to identify data distributions that change over software versions. The proposed CDD framework consists of three stages: (i) data pre-processing for CD detection; (ii) notification of CD by triggering one of the three flags (i.e., CD, warning, and control); and (iii) providing guidance on when to update an existing model. Several experiments on 30 versions of seven software projects reveal the value of the proposed CDD. Some of the key findings of the proposed work include: (i) An exponential increase in the error-rate across different software versions is associated with CD. (ii) A moving-window approach to train defect prediction models on chronologically ordered defect data results in better CD detection than using all historical data with a large effect size (Formula presented.).