In-Depth Analysis of Various Artificial Intelligence Techniques in Software Engineering: Experimental Study
In this paper, we have extended our literature survey with experimental implementation. Analyzing numerous Artificial Intelligence (AI) techniques in software engineering (SE) can help understand the field better; the outcomes will be more effective when used with it. Our manuscript shows various AI...
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| Format: | Article |
| Language: | English |
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University of Tehran
2023-08-01
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| Series: | Journal of Information Technology Management |
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| Online Access: | https://jitm.ut.ac.ir/article_93632_166ce2610938e952504410149d73808d.pdf |
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| author | Mohd Mustaqeem Tamanna Siddiqui Najeeb Ahmad Khan Deepak Kumar |
| author_facet | Mohd Mustaqeem Tamanna Siddiqui Najeeb Ahmad Khan Deepak Kumar |
| author_sort | Mohd Mustaqeem |
| collection | DOAJ |
| description | In this paper, we have extended our literature survey with experimental implementation. Analyzing numerous Artificial Intelligence (AI) techniques in software engineering (SE) can help understand the field better; the outcomes will be more effective when used with it. Our manuscript shows various AI-based algorithms that include Machine learning techniques (ML), Artificial Neural Networks (ANN), Deep Neural Networks (DNN) and Convolutional Neural Networks (CNN), Natural Language Processing (NLP), Genetic Algorithms (GA) applications. Software testing using Ant Colony Optimization (ACO) approach, predicting software maintainability with Group Method of Data Handling (GMDH), Probabilistic Neural Network (PNN), and Software production with time series analysis technique. Furthermore, data is the fuel for AI-based model testing and validation techniques. We have also used NASA dataset promise repository in our script. There are various applications of AI in SE, and we have experimentally demonstrated one among them, i.e., software defect prediction using AI-based techniques. Moreover, the expected future trends have also been mentioned; these are some significant contributions to the research |
| format | Article |
| id | doaj-art-ac6c9186006b492db797083b62e1ddcc |
| institution | OA Journals |
| issn | 2008-5893 2423-5059 |
| language | English |
| publishDate | 2023-08-01 |
| publisher | University of Tehran |
| record_format | Article |
| series | Journal of Information Technology Management |
| spelling | doaj-art-ac6c9186006b492db797083b62e1ddcc2025-08-20T01:59:52ZengUniversity of TehranJournal of Information Technology Management2008-58932423-50592023-08-0115316218110.22059/jitm.2023.9363293632In-Depth Analysis of Various Artificial Intelligence Techniques in Software Engineering: Experimental StudyMohd Mustaqeem0Tamanna Siddiqui1Najeeb Ahmad Khan2Deepak Kumar3Ph.D. Scholar, Department of Computer Science, Science, Aligarh Muslim University (AMU), Aligarh, U.P, India.Professor, Department of Computer Science, Aligarh Muslim University (AMU), Aligarh, U.P, India.Associate Professor, Faculty of Engineering & Technology at Arunachal University of Studies, Namsai, Arunachal Pradesh, India.Professor, Amity University Uttar Pradesh, Noida, India.In this paper, we have extended our literature survey with experimental implementation. Analyzing numerous Artificial Intelligence (AI) techniques in software engineering (SE) can help understand the field better; the outcomes will be more effective when used with it. Our manuscript shows various AI-based algorithms that include Machine learning techniques (ML), Artificial Neural Networks (ANN), Deep Neural Networks (DNN) and Convolutional Neural Networks (CNN), Natural Language Processing (NLP), Genetic Algorithms (GA) applications. Software testing using Ant Colony Optimization (ACO) approach, predicting software maintainability with Group Method of Data Handling (GMDH), Probabilistic Neural Network (PNN), and Software production with time series analysis technique. Furthermore, data is the fuel for AI-based model testing and validation techniques. We have also used NASA dataset promise repository in our script. There are various applications of AI in SE, and we have experimentally demonstrated one among them, i.e., software defect prediction using AI-based techniques. Moreover, the expected future trends have also been mentioned; these are some significant contributions to the researchhttps://jitm.ut.ac.ir/article_93632_166ce2610938e952504410149d73808d.pdfsoftware engineeringdefects predictionartificial intelligencemlanndnncnn |
| spellingShingle | Mohd Mustaqeem Tamanna Siddiqui Najeeb Ahmad Khan Deepak Kumar In-Depth Analysis of Various Artificial Intelligence Techniques in Software Engineering: Experimental Study Journal of Information Technology Management software engineering defects prediction artificial intelligence ml ann dnn cnn |
| title | In-Depth Analysis of Various Artificial Intelligence Techniques in Software Engineering: Experimental Study |
| title_full | In-Depth Analysis of Various Artificial Intelligence Techniques in Software Engineering: Experimental Study |
| title_fullStr | In-Depth Analysis of Various Artificial Intelligence Techniques in Software Engineering: Experimental Study |
| title_full_unstemmed | In-Depth Analysis of Various Artificial Intelligence Techniques in Software Engineering: Experimental Study |
| title_short | In-Depth Analysis of Various Artificial Intelligence Techniques in Software Engineering: Experimental Study |
| title_sort | in depth analysis of various artificial intelligence techniques in software engineering experimental study |
| topic | software engineering defects prediction artificial intelligence ml ann dnn cnn |
| url | https://jitm.ut.ac.ir/article_93632_166ce2610938e952504410149d73808d.pdf |
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