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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.
Automatic Software Refactoring to Enhance Quality: A Review Khaleel, Shahbaa I.; Mahmood , Rasha Ahmed
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 4 (2024): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i4.30277

Abstract

Refactoring aims to enhance the internal structure of the code and improve maintainability without affecting its functionality and external behavior. As a result of the development of technologies, it has become necessary to apply automatic refactoring to address complexities and reduce technical debt. This review presents machine learning and deep learning techniques that lead to identifying opportunities for the need for refactoring and implementing them through analyzing the software code and discovering "code smells", where the focus is on the role of tools such as RefactoringMiner, CODEBERT in enhancing the accuracy of prediction. This review presents various methodologies that include metrics-based methods, search, machine learning and discusses their impact on software quality. The review reviews experimental studies that focus on the challenges of refactoring such as reducing the risks associated with making unnecessary modifications and determining the appropriate timing. Notable empirical studies include a study by Bavota et al., in which Ref-Finder was used to detect 15,008 refactorings in open source software systems, identifying 85% of which improved code quality and reduced bugs. Additionally, another study by Khatchadourian et al. demonstrated the effectiveness of OPTIMIZE STREAMS in improving code performance in large Java projects, increasing efficiency by 55% on average. The study presents two research contributions. The first is a comprehensive analysis of automated refactoring techniques using machine learning algorithms, in addition to improving maintainability and reducing complexity. The second contribution is to provide recommendations to support developers in using modern tools and choosing the right timing for refactoring, which enhances code productivity. The results showed that machine learning techniques can significantly enhance the efficiency of refactoring and thus support developers in making accurate decisions in enhancing maintainability.
Co-Authors Ahmed, Rasha Mahmood , Rasha Ahmed Salih, Lumia Faiz