Abeer Salim Jamil

Background : Artificial Intelligence (AI) has recently emerged as a transformative innovation within the software industry, disrupting conventional approaches to application development by automating tasks, refining code, and enhancing resource efficiency. Prior research indicates the effectiveness of AI-powered tools across various domains. However, contemporary studies lack a detailed analysis of the diverse sectors utilizing AI tools for software development. Objective : This article aims to identify the potential benefits and impacts of AI in software development, specifically regarding time-to-market, productivity, code quality, bug-fixing rates, resource flexibility, and developer satisfaction. The goal is to present fact-based information about AI’s impact on multiple industries and scopes of work. Methods : A mixed-methods research design was employed to analyze quantitative data from 40 projects across healthcare, financial services, retail, technology, and e-commerce industries. Data were collected using various project management tools, automated testing environments, and online questionnaires addressed to developers. The study incorporated a comparative evaluation of AI-based projects and traditional projects, with statistical analysis. Results : AI-driven software development projects demonstrated a mean reduction in time-to-market by 34.6%, an improvement in code quality by 70%, and a mean reduction in bug-fixing time by 57.7%. Productivity per sprint increased by over 70%, resource flexibility was higher (90.2% in AI projects vs. 67.8% in traditional projects), and developers reported higher satisfaction levels. These findings reinforce the concept that AI significantly enhances workflow and the achievement of optimal results. Conclusion : AI substantially improves both the speed and quality of software development. Further research should expand to explore the experiences of different sectors, the application of AI-driven tools, their differentiation, and usage, as well as the ethical considerations to promote sustainable and innovative software engineering solutions.

Background : Security has been a major issue of discussion due to increase in the number and sophistication of Cyber threats in the modern era. Conventional approaches to threat identification might face difficulties in a number of things, namely the relevancy and the ability to process new and constantly evolving threats. Machine learning (ML) and deep learning (DL) based Approaches present AI as a potential solution to the problem of efficient threat detection.   Objective : The article aims to compare the RF, SVM, CNNs, and RNNs models’ performance, computational time, and resilience in identifying potential cyber threats, such as malware, phishing, and DoS attacks.   Methods : The proposed models were trained as well as evaluated on the NSL-KDD and CICIDS 2017 datasets. This was done based on common scheme indicators including accuracy, precision, recollection, F1 measure, detection rate of efficiency, AUC-ROC, False Alarm Rate (FAR), and the stability to adversaries. Rating of computational efficiency was defined by training time and memory consumption.   Results : The findings indicate that the CNNs gave the best accuracy (96%) and resisted perturbation better, and the RF showed good performance with little computational load. RNNs have been proved effective in sequential data analysis and SVM also performed fairly well on binary data classification although there is a problem of scalability.   Conclusion : CNNs used in AI models are the best solutions to protection from the threats in the cybersecurity space. Nevertheless, some of them still require computational optimization in order to make those beneficial in scenarios with a limited usage of computational resources. It is suggested that these findings can be used in the context of subsequent research and practical applications.