{"id":3895,"date":"2023-04-24T16:55:38","date_gmt":"2023-04-24T12:25:38","guid":{"rendered":"http:\/\/arrahimipour.com\/blog-2\/"},"modified":"2025-11-17T18:12:27","modified_gmt":"2025-11-17T14:42:27","slug":"blog","status":"publish","type":"page","link":"https:\/\/arrahimipour.com\/en\/home\/blog\/","title":{"rendered":"posts"},"content":{"rendered":"\t\t
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\r\n \r\n Design and Implementation of an Intelligent Framework for Automated Testing of Web Applications Using Artificial Intelligence <\/a>\r\n <\/h3>\r\n\r\n
\r\n \r\n 10<\/strong>\r\n Aug <\/span>\r\n

With the increasing complexity of web applications, the need for comprehensive and effective automated testing has become more important than ever. This paper reviews and analyzes recent approaches to automated testing of web applications using artificial intelligence algorithms, including machine learning, deep learning, and neural networks. After examining the challenges of traditional web testing, such […]<\/p>\r\n <\/div>\r\n\r\n <\/div>\r\n <\/div>\r\n\r\n\r\n

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\r\n \r\n Application of Machine Learning in Predicting Software Faults During the Early Stages of Complex Systems Development <\/a>\r\n <\/h3>\r\n\r\n
\r\n \r\n 10<\/strong>\r\n Aug <\/span>\r\n

Machine learning has emerged as an effective approach for predicting software defects in the early stages of developing complex systems. In this study, historical project statistics and code metrics were collected and preprocessed, while initial software class labels were generated using K-means clustering. Several classification algorithms, including Support Vector Machine (SVM), Random Forest, Naive Bayes, […]<\/p>\r\n <\/div>\r\n\r\n <\/div>\r\n <\/div>\r\n\r\n\r\n

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\r\n \r\n Resource Optimization in Large Language Model Deployment Using Reinforcement Learning and Adaptive Software Engineering <\/a>\r\n <\/h3>\r\n\r\n
\r\n \r\n 10<\/strong>\r\n Aug <\/span>\r\n

Large Language Models (LLMs) are extremely resource-intensive to deploy, demanding high memory and compute. Static provisioning often leads to waste or unmet demand. We propose a conceptual framework that uses reinforcement learning (RL) and self-adaptive software engineering to optimize resource use in LLM deployments. An RL agent monitors system metrics (throughput, latency, GPU\/CPU utilization) and […]<\/p>\r\n <\/div>\r\n\r\n <\/div>\r\n <\/div>\r\n\r\n\r\n

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\r\n \r\n Analysis and Prediction of Software Bugs Using Deep Learning Models and Code Dependency Graphs <\/a>\r\n <\/h3>\r\n\r\n
\r\n \r\n 10<\/strong>\r\n Aug <\/span>\r\n

Software bug prediction aims to automatically identify code modules likely to contain defects, improving quality and reducing testing costs. Traditional methods use static code metrics (complexity, churn) with classical machine learning, but these often ignore inter-module relationships. Recently, deep learning (DL) techniques have shown promise by learning features from code text and structure. In particular, […]<\/p>\r\n <\/div>\r\n\r\n <\/div>\r\n <\/div>\r\n\r\n\r\n

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\r\n \r\n Designing Hybrid Methodologies in Artificial Intelligence for Solving Complex Problems <\/a>\r\n <\/h3>\r\n\r\n
\r\n \r\n 04<\/strong>\r\n Aug <\/span>\r\n

Complex problems are usually characterized by nonlinear nature, multiple criteria, and large-scale data, making them difficult to solve using a single artificial intelligence technique. This paper examines the importance of employing hybrid methods in artificial intelligence to improve the accuracy and efficiency of solving complex problems. A hybrid approach refers to the simultaneous or sequential […]<\/p>\r\n <\/div>\r\n\r\n <\/div>\r\n <\/div>\r\n\r\n\r\n

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\r\n \r\n Enhancing Security and Privacy in the Internet of Things: The Transformative Role of Blockchain Technologies <\/a>\r\n <\/h3>\r\n\r\n
\r\n \r\n 04<\/strong>\r\n Aug <\/span>\r\n

The Internet of Things (IoT) refers to a network of smart devices that extensively exchange data. The rapid growth of this technology, alongside its many benefits, has also created major challenges in the areas of security and privacy. Traditional cybersecurity methods, due to their centralized structure and vulnerability to emerging threats, are insufficient for protecting […]<\/p>\r\n <\/div>\r\n\r\n <\/div>\r\n <\/div>\r\n\r\n\r\n

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\r\n \r\n Applying Machine Learning to Optimize Software Runtime and Memory Usage <\/a>\r\n <\/h3>\r\n\r\n
\r\n \r\n 24<\/strong>\r\n Jul <\/span>\r\n

Software systems today face increasing demands for high performance and efficient memory usage. Traditional optimization methods, often hard-coded and inflexible, struggle to adapt to complex and dynamic workloads. Machine Learning (ML) algorithms offer a promising approach by automatically learning patterns and making intelligent decisions to optimize execution time (runtime) and memory consumption. This paper provides […]<\/p>\r\n <\/div>\r\n\r\n <\/div>\r\n <\/div>\r\n\r\n\r\n

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\r\n \r\n Automatic Code Evolution Using Advanced Language Models in Software Development <\/a>\r\n <\/h3>\r\n\r\n
\r\n \r\n 11<\/strong>\r\n Jul <\/span>\r\n

With the advancement of Large Language Models (LLMs) in recent years, automatic code completion has become one of their key applications in software development. This paper examines automatic code evolution with a focus on the Python programming language. In this study, state-of-the-art language models such as GPT-4, OpenAI Codex, and tools such as GitHub Copilot […]<\/p>\r\n <\/div>\r\n\r\n <\/div>\r\n <\/div>\r\n\r\n\r\n <\/div>\r\n \t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

My posts Articles Harnessing Reinforcement Learning to Revolutionize Software Development 13 \u0628\u0647\u0645\u0646 In this article, I explore how Reinforcement Learning (RL) is reshaping the landscape of software development. From optimizing code quality to automating CI\/CD pipelines, RL is paving the way for more efficient, intelligent software engineering. I discuss its challenges, real-world applications, and future […]<\/p>\n","protected":false},"author":3,"featured_media":0,"parent":3907,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"page-elementor.php","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-3895","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/arrahimipour.com\/en\/wp-json\/wp\/v2\/pages\/3895","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/arrahimipour.com\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/arrahimipour.com\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/arrahimipour.com\/en\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/arrahimipour.com\/en\/wp-json\/wp\/v2\/comments?post=3895"}],"version-history":[{"count":0,"href":"https:\/\/arrahimipour.com\/en\/wp-json\/wp\/v2\/pages\/3895\/revisions"}],"up":[{"embeddable":true,"href":"https:\/\/arrahimipour.com\/en\/wp-json\/wp\/v2\/pages\/3907"}],"wp:attachment":[{"href":"https:\/\/arrahimipour.com\/en\/wp-json\/wp\/v2\/media?parent=3895"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}