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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI)
Khaleel, Shahbaa I.
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Computer Science & IT Electrical & Electronics Engineering Engineering

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A survey of predicting software reliability using machine learning methods Khaleel, Shahbaa I.; Salih, Lumia Faiz
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp35-44

Abstract

In light of technical and technological progress, software has become an urgent need in every aspect of human life, including the medicine sector and industrial control. Therefore, it is imperative that the software always works flawlessly. The information technology sector has witnessed a rapid expansion in recent years, as software companies can no longer rely only on cost advantages to stay competitive in the market, but programmers must provide reliable and high-quality software, and in order to estimate and predict software reliability using machine learning and deep learning, it was introduced A brief overview of the important scientific contributions to the subject of software reliability, and the researchers' findings of highly efficient methods and techniques for predicting software reliability. 
Enhancing Refactoring Prediction at the Method-Level Using Stacking and Boosting Models Khaleel, Shahbaa I.; Ahmed, Rasha
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i2.30839

Abstract

Refactoring software code is crucial for developers since it enhances code maintainability and decreases technical complexity. The existing manual approach to refactoring demonstrates restricted scalability because of its requirement for substantial human intervention and big training information. A method-level refactoring prediction technique based on meta-learning uses classifier stacking and boosting and Lion Optimization Algorithm (LOA) for feature selection. The evaluation of the proposed model used four Java open source projects namely JUnit, McMMO, MapDB, and ANTLR4 showing exceptional predictive results. The technique successfully decreased training data necessities by 30% yet generated better prediction results by 10–15% above typical models to deliver 100% accuracy and F1 scores on DTS3 and DTS4 datasets. The system decreased incorrect refactoring alert counts by 40% which lowered the amount of needed developer examination.
Co-Authors Ahmed, Rasha Salih, Lumia Faiz