In-Depth Analysis of Various Artificial Intelligence Techniques in Software Engineering: Experimental Study

  • سال انتشار: 1402
  • محل انتشار: فصلنامه مدیریت فناوری اطلاعات، دوره: 15، شماره: 3
  • کد COI اختصاصی: JR_JITM-15-3_010
  • زبان مقاله: انگلیسی
  • تعداد مشاهده: 105
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نویسندگان

Mustaqeem

Ph.D. Scholar, Department of Computer Science, Science, Aligarh Muslim University (AMU), Aligarh, U.P, India.

Siddiqui

Professor, Department of Computer Science, Aligarh Muslim University (AMU), Aligarh, U.P, India.

Ahmad Khan

Associate Professor, Faculty of Engineering & Technology at Arunachal University of Studies, Namsai, Arunachal Pradesh, India.

Kumar

Professor, Amity University Uttar Pradesh, Noida, India.

چکیده

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

کلیدواژه ها

Software Engineering, Defects Prediction, Artificial Intelligence, ML, ANN, DNN, CNN

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