2026· International Journal of Advanced Computer Science and Applications· Vol 17· 0 citations· 32 references
TL;DR
This study presents a comprehensive survey and comparative analysis of AI-based techniques for error detection and automated program repair, revealing that while LLM-based approaches significantly outperform traditional methods in repair capability, challenges remain in patch correctness validation, computational cost, and generalization to real-world codebases.
Abstract
Software bugs remain one of the most costly chal-lenge in software engineering, consuming significant development time and resources. Recent advances in Artificial Intelligence (AI), particularly deep learning and large language models (LLMs), have shown remarkable potential in automating the detection and repair of software errors. This study presents a comprehensive survey and comparative analysis of AI-based techniques for error detection and automated program repair (APR). We catego-rize existing approaches into traditional search-based methods, learning-based neural machine translation models, and emerg-ing LLM-based repair systems. We evaluate these techniques across standard benchmarks, including Defects4J and SWE-bench, comparing their effectiveness in terms of bugs fixed, patch correctness, and scalability. Our analysis reveals that while LLM-based approaches significantly outperform traditional methods in repair capability, challenges remain in patch correctness validation, computational cost, and generalization to real-world codebases. Our results show that LLM-based tools, such as ChatRepair, can correctly fix 114 out of 395 benchmark bugs at just $0.42 per fix, fixing 2.6× as many bugs as the best traditional method (a 165% increase). We discuss open challenges and propose future research directions toward more reliable AI-assisted software development.
Current research is summarized to identify key gaps and future directions to optimize LLM based APR are proposed, to assure its reliability and scalability in real world software development.
Fatmaelzahra Hamdi, Ramadam Moawad, A. Mohsen· Journal of universal compute...· 0 citations
In Ensuring fast and precise code review process has become one of the major problems of the modern DevOps world, where software development happens quickly for code reviews to be done manually. In this paper, we introduce a novel automated code review framework named AICR-DevOps, which utilizes a combination of rule-based static analysis, fine-tuned CodeBERT semantic classifier, and large language model using confidence-based aggregation. The framework architecture allows successfully combining the power of traditional program analysis approaches and artificial intelligence reasoning capabilities to achieve high review accuracy with minimized false positives. Together with GitHub Actions, the framework is capable of conducting pull request analysis instantly and constantly adapting to specific coding practices of projects by fine-tuning based on developer feedback using LoRA method. Experimentation with the proposed framework on 12,400 pull requests gathered from 40 Java and Python repositories reached 84.7% precision, 81.3% recall, and an F1-score of 82.9%, with review comments provided in 38 seconds on average. Moreover, a controlled study conducted with 48 developers showed that the acceptance rate of the developers to the framework reached 73.6%.
Nawnit Kumar, M. K. Shukla, Raushendra Kumar et al.· 2026 6th International Confe...· 0 citations
The intelligent bug prediction system addresses the main problem of identifying the defect-prone software modules in the early stages of the software development life cycle. The conventional testing methodologies are time-consuming and costly and do not provide an effective way to prioritise high-risk modules. This problem is important for improving software reliability, reducing maintenance cost and improving the overall quality of software systems. The current bug prediction techniques mainly rely on traditional machine learning models such as Random Forests, Support Vector Machines (SVMs), and Neural Networks, but they face challenges such as imbalanced data, limited feature sets, low interpretability, and binary predictions that do not provide meaningful guidance for testing prioritisation.In our approach, we propose an intelligent machine learning-based bug prediction framework that uses SMOTE for dataset balancing and feature selection to identify the most relevant software metrics. We also use advanced ensemble learning techniques, such as CatBoost, LightGBM, and the Stacking Ensemble model, to improve prediction accuracy. Methodology: Analysed software metrics from NASA MDP evaluated model performance using Accuracy, Precision, Recall, F1-score, ROC-AUC, and Confusion Matrix; and applied SHAP (SHapley Additive Explanations) to provide transparent and interpretable predictions. A Risk Scoring Mechanism categorises software modules as Stable, High, Medium, or Low risk, and Bug Fix Recommendations help developers more effectively fix predicted defects.The proposed system is expected to produce accurate, explainable, and risk-aware bug predictions. This enables developers to prioritise testing efforts, optimise resource allocation, cut debugging costs, and deliver more dependable, secure, and high-quality software.
Bhukya Yashaswini· International Journal of Eng...· 0 citations
Code smells are indicative of poor software design, maintainability issues, or potential defects, and their early detection is critical for high-quality software development. Traditional detection methods rely heavily on manual inspection or rule-based static analysis, which are often time-consuming, error-prone, and limited in adaptability. This paper explores the use of machine learning techniques for automated code smell detection and resolution. By leveraging code metrics, syntactic and semantic features, and historical refactoring data, ML models can identify patterns associated with common code smells and recommend targeted resolution strategies. The proposed approach is evaluated on open-source and industrial software projects, demonstrating improvements in detection accuracy, maintainability, and developer productivity. The study highlights the potential of intelligent, data-driven approaches to enhance software quality and support continuous code improvement.
Emily Johnson· International Journal of Mac...· 0 citations
An application-focused review of 35 selected empirical studies focusing on the use of AI during software testing, based on PRISMA guidelines, reveals that large language models, machine learning, and computer vision can significantly improve testing efficiency.
Guilherme Martins, Nelson N. Tenório, Jorge Bernardino· Big Data and Cognitive Compu...· 1 citation
Software reliability remains a major concern in modern software engineering due to the increasing complexity of software systems and the rapid pace of development. Software Failure Prediction (SFP) aims to identify fault-prone modules before deployment, enabling organizations to reduce maintenance costs and improve system quality. Artificial Intelligence (AI) and Machine Learning (ML) techniques provide data-driven approaches for analyzing software metrics, defect repositories, and execution logs to predict failures. This review examines AI/ML-based approaches for software failure prediction and efficiency optimization, with explicit attention to benchmark datasets, dataset quality, preprocessing, class imbalance, feature selection, model families, validation strategies, and evaluation measures. Representative NASA and PROMISE/Jureczko datasets are characterized in terms of software-unit type, metric families, binary defect labels, and imbalance. The review emphasizes Precision, Recall, F1-score, ROC-AUC, and MCC in addition to accuracy and compares traditional ML, ensemble, and deep-learning approaches. It further discusses missing data, concept drift, and cross-project prediction as key factors affecting real-world generalization. The analysis indicates that no single model is universally optimal; robust software failure prediction requires dataset-aware preprocessing, leakage-safe validation, imbalance-aware evaluation, and an explicit trade-off among predictive performance, computational efficiency, and interpretability.
Prof. Prasad Mathapati, Dr.Shantappa G Gollagi, Prof. Zebashireen Fahim Shaikh· International journal of res...· 0 citations