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Journal articles on the topic 'Automated Bug Detection'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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1

Emeto, I. C., U. W. Anthony, A. A. Galadima, et al. "A WEB-BASED AUTOMATED BUG DETECTION AND FIX SUGGESTION SYSTEM." International Journal of Computer Science and Mobile Computing 14, no. 3 (2025): 28–38. https://doi.org/10.47760/ijcsmc.2025.v14i03.004.

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Software bugs are a persistent and costly challenge in software development, with organizations spending billions annually on debugging efforts. Traditional bug detection systems are often manual, resulting in inefficiencies and delayed resolutions. This research introduces a web-based automated bug detection and fix suggestion system that addresses these limitations. The system utilizes a dynamic database, Python-based automation, and a recommender engine to detect bugs in real time and propose context-aware fixes. Key innovations include a user-friendly reporting interface, integration of hi
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K S, Ms Surabhi. "Real Time Bug Detection Using Machine Learning Algorithm." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42112.

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Real-time bug detection in software systems is a critical aspect of software quality assurance. Traditional debugging techniques often fail to scale with the increasing complexity of software. Machine learning (ML) offers a promising solution for automated and real-time bug detection. This paper explores various machine learning algorithms used for bug detection, their implementation in real-time systems using Python, and the challenges faced. We also discuss performance evaluation metrics and future directions in the field.
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ZOU, Jie, Ling XU, Mengning YANG, Xiaohong ZHANG, Jun ZENG, and Sachio HIROKAWA. "Automated Duplicate Bug Report Detection Using Multi-Factor Analysis." IEICE Transactions on Information and Systems E99.D, no. 7 (2016): 1762–75. http://dx.doi.org/10.1587/transinf.2016edp7052.

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Chen, Mengzhuo, Zhe Liu, Chunyang Chen, et al. "Standing on the Shoulders of Giants: Bug-Aware Automated GUI Testing via Retrieval Augmentation." Proceedings of the ACM on Software Engineering 2, FSE (2025): 825–46. https://doi.org/10.1145/3715755.

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In software development, similar apps often encounter similar bugs due to shared functionalities and implementation methods. However, current automated GUI testing methods mainly focus on generating test scripts to cover more pages by analyzing the internal structure of the app, without targeted exploration of paths that may trigger bugs, resulting in low efficiency in bug discovery. Considering that a large number of bug reports on open source platforms can provide external knowledge for testing, this paper proposes BugHunter, a novel bug-aware automated GUI testing approach that generates ex
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Nam, Seong-Guk, and Yeong-Seok Seo. "GUI Component Detection-Based Automated Software Crash Diagnosis." Electronics 12, no. 11 (2023): 2382. http://dx.doi.org/10.3390/electronics12112382.

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This study presents an automated software crash-diagnosis technique using a state transition graph (STG) based on GUI-component detection. An STG is a graph representation of the state changes in an application that are caused by actions that are executed in the GUI, which avoids redundant test cases and generates bug-reproduction scenarios. The proposed technique configures the software application STG using computer vision and artificial intelligence technologies and performs automated GUI testing without human intervention. Four experiments were conducted to evaluate the performance of the
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Ali, Waqas, Saima siraj Soomro, Shamshad Lakho, Nadeem Naeem Bhatti, and Imran Ali Memon. "Adaptive Bug Localization Framework for Precision-Driven Bug Localization in Software Engineering." VFAST Transactions on Software Engineering 12, no. 3 (2024): 230–42. http://dx.doi.org/10.21015/vtse.v12i3.1832.

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Software development always looks for automated methods to improve productivity and accuracy in issue detection. The paper conducts a comparative examination of several machine-learning techniques to tackle the bug localization difficulty. Our study compared the performance of Logistic Regression (LR), Random Forest Classifier (RFC), Support Vector Machine (SVM), Gradient Boosting Classifier (GBC), and Adaptive Bug Localization System (ABLS) on five dataset versions. The results demonstrate the superior performance of ensemble learning methods. The ABLS model regularly beats other models regar
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Fezzardi, Pietro, Fabrizio Ferrandi, and Christian Pilato. "Enabling Automated Bug Detection for IP-Based Designs Using High-Level Synthesis." IEEE Design & Test 35, no. 5 (2018): 54–62. http://dx.doi.org/10.1109/mdat.2018.2824121.

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Fezzardi, Pietro, and Fabrizio Ferrandi. "Automated Bug Detection for High-level Synthesis of Multi-threaded Irregular Applications." ACM Transactions on Parallel Computing 7, no. 4 (2020): 1–26. http://dx.doi.org/10.1145/3418086.

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Rathnasuriya, Ravishka, Nidhi Majoju, Zihe Song, and Wei Yang. "An Investigation on Numerical Bugs in GPU Programs Towards Automated Bug Detection." Proceedings of the ACM on Software Engineering 2, ISSTA (2025): 1654–77. https://doi.org/10.1145/3728950.

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General-purpose graphics processing unit (GPU) computing has emerged as a leading parallel computing paradigm, offering significant performance gains in various domains such as scientific computing and deep learning. However, GPU programs are susceptible to numerical bugs, which can lead to incorrect results or crashes. These bugs are difficult to detect, debug, and fix due to their dependence on specific input values or types and the absence of reliable error-checking mechanisms and oracles. Additionally, the unique programming conventions of GPUs complicate identifying the root causes of bug
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Arivoli, Anbarasu. "AI FOR AUTOMATED BUG DETECTION AND DEBUGGING: A COMPARATIVE STUDY OF CURRENT APPROACHES." International Journal of Business Quantitative Economics and Applied Management Research 7, no. 12, 2024 (2024): 57–66. https://doi.org/10.5281/zenodo.15236719.

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This paper explores the use of artificial intelligence, particularly machine learning and deep learning, in enhancing software debugging. It compares traditional methods with AI-driven approaches like reinforcement learning, highlighting their adaptability, efficiency, and scalability in large-scale systems. The study also discusses challenges such as computational costs and model interpretability while proposing optimizations for real-time debugging. It envisions AI as a powerful support tool that improves defect detection and resolution, ultimately enabling faster, more secure, and reliable
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Pailoor, Shankara, Yanju Chen, Franklyn Wang, et al. "Automated Detection of Under-Constrained Circuits in Zero-Knowledge Proofs." Proceedings of the ACM on Programming Languages 7, PLDI (2023): 1510–32. http://dx.doi.org/10.1145/3591282.

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As zero-knowledge proofs gain increasing adoption, the cryptography community has designed domain-specific languages (DSLs) that facilitate the construction of zero-knowledge proofs (ZKPs). Many of these DSLs, such as Circom, facilitate the construction of arithmetic circuits, which are essentially polynomial equations over a finite field. In particular, given a program in a zero-knowledge proof DSL, the compiler automatically produces the corresponding arithmetic circuit. However, a common and serious problem is that the generated circuit may be underconstrained, either due to a bug in the pr
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Joshi, Prof Indira. "Bug Tracking System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem32737.

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For many years, bug-tracking mechanisms have been employed only in some of the large software development houses. Most of the others never bothered with bug tracking at all, and instead simply relied on shared lists and email to monitor the status of defects. This procedure is error-prone and tends to cause those bugs judged least significant by developers to be dropped or ignored. Bug Tracking System is an ideal solution to track the bugs of a product, solution or an application. Bug Tracking System allows individuals or groups of developers to keep track of outstanding bugs in their product
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Fu, Haojie, Zan Wang, Xiang Chen, and Xiangyu Fan. "A systematic survey on automated concurrency bug detection, exposing, avoidance, and fixing techniques." Software Quality Journal 26, no. 3 (2017): 855–89. http://dx.doi.org/10.1007/s11219-017-9385-3.

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Mishra, Anurag. "Mindfulness and Emotional Intelligence: A Study on Millennials on Hospitality." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem32337.

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In software development, dependability and quality are critical. A Bug Tracking System (BTS) designed specifically for large-scale software projects is introduced in this significant project with the goal of streamlining bug tracking, reporting, detection, and resolution. The system provides secure authentication, a backend database that is reliable, and an easy-to-use web interface. Using cutting-edge web technologies, it offers critical features including automated notifications, progress tracking, severity assignment, and thorough bug reporting together with user-friendly interfaces for a r
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Thomas, Alex Thomas. "Review of AI-Driven Approaches for Automated Defect Detection and Classification in Software Testing." International Journal of Research and Review 12, no. 6 (2025): 154–60. https://doi.org/10.52403/ijrr.20250619.

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Increased size and complexity of modern software systems have necessitated novel, smart approaches to detecting and classifying defects. Within this review, we provide a comprehensive perspective on artificial intelligence (AI)-driven techniques within the umbrella of automated software testing. Relying on up-to-date research in machine learning, deep learning, and natural language processing, we explore how these technologies enhance accuracy, efficiency, and scalability of defect identification approaches. Studies such as VulDeePecker and other deep learning frameworks have revealed signific
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Wang, Shikai, Haotian Zheng, Xin Wen, Kangming Xu, and Hao Tan. "Enhancing chip design verification through AI-powered bug detection in RTL code." Applied and Computational Engineering 92, no. 1 (2024): 27–33. http://dx.doi.org/10.54254/2755-2721/92/20241685.

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This paper presents a novel AI-driven approach for enhancing chip design verification through automated bug detection in Register Transfer Level (RTL) code. The proposed method integrates advanced machine learning techniques with domain-specific knowledge of chip design to address the challenges of increasing complexity and time-to-market pressures in modern integrated circuit development. Our system employs a comprehensive data preprocessing pipeline that effectively captures syntactic and semantic features of RTL code, feeding into an innovative attention-based neural network model. The mode
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Singh, Yogesh Dev. "Review on Structural Software Testing Coverage Approaches." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 2502–8. http://dx.doi.org/10.22214/ijraset.2021.34821.

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Testing is broadly classified into three levels: Unit Testing, Addition Testing, and System Testing. Whenever we think of developing any software we always concentrate on making the software bug free and most reliable. At this point of time Testing is used to make the software a bug free. Software Testing has been measured as the most important stage of the software development life cycle. Around 60% of resources and money are cast-off for the testing of software. Testing can be manual or automated. Software testing is an activity that emphases at assessing the competence of a program and comm
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Kumar, Manish. "AIRA : AI-Powered Code Review & Bug Detection System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42592.

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The increasing complexity of software develop- ment presents significant challenges for developers, including bug detection, code inefficiencies, and security vulnerabilities. Traditional methods of code review and debugging often result in increased workload and reduced productivity. To address these issues, AI-powered tools are emerging as a solution to enhance code quality, streamline development, and minimize human error. Introducing AIRA (AI-powered Intelligent Review Assistant), an advanced AI-driven code review and bug detection system designed to assist developers in improving code qua
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Arora, Amandeep Singh, and Dr Linesh Raja. "Design And Implementation Of Bug Resolution Prediction Scheme On Hpc Systems Through Large-Scale Log Analysis." International Journal of Environmental Sciences 11, no. 10s (2025): 870–85. https://doi.org/10.64252/akd8kk57.

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In the realm of High-Performance Computing (HPC), where computational power propels scientific advancement to new heights, the complexity and scale of modern systems bring forth a formidable challenge - the mitigation of software bugs and performance bottlenecks. Addressing this issue necessitates proactive and automated methodologies that enable bug prediction and resolution before their disruptive consequences emerge. To this end, we present an innovative Bug Resolution Prediction Scheme (BRPS), leveraging large-scale log analysis as the cornerstone of its predictive prowess. Our meticulousl
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20

Santosh, Kumar Jawalkar. "AI-Driven Bug Hunting: Leveraging Machine Learning for Predictive Defect Detection in AR/VR." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 11, no. 5 (2023): 1–19. https://doi.org/10.5281/zenodo.14945225.

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The emergence of AR (Augmented Reality) and VR (Virtual Reality) technologies is reshaping several industries, yet these advancements entail considerable hurdles, especially regarding defect detection, performance bottlenecks, and real-time debugging. In the challenge of ensuring the performance of the AR/VR application, traditional approaches like manual testing and performance profiling are often unable to cope with the complexity and dynamicity of AR/VR systems. Our work focuses on utilizing Artificial Intelligence (AI) and Machine Learning (ML) models as automated means for
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R. Padmavathi. "Insect Identification System using Faster RCNN with ADAM optimizer Segmentation model." Communications on Applied Nonlinear Analysis 32, no. 5s (2024): 361–75. https://doi.org/10.52783/cana.v32.3107.

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Researchers have been more interested in automated insect recognition in recent years, and many different approaches have been taken to studying the practical implications of this field. When it comes to designing pest management tactics and safeguarding beneficial insects, accurate identification of the insects at play is crucial. Insect target detection has always relied heavily on artificial identification methods; however, deep learning can automatically extract characteristics for detection, solving the issue of poor detection accuracy due to subjective considerations. Here, we present ou
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Ratnangi, Nirek. "Impact of Continuous Integration and Continuous Deployment (CI/CD) on Software Quality and Delivery Speed in Linux Systems." European Journal of Advances in Engineering and Technology 6, no. 8 (2019): 95–99. https://doi.org/10.5281/zenodo.13919415.

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Continuous Integration (CI) and Continuous Deployment (CD) have become pivotal processes in modern software development, aiming to enhance software quality and expedite delivery. By evaluating case studies, reviewing literature, and analyzing industry reports, we explore how CI/CD processes enable automated testing, frequent code integration, and rapid deployment. Furthermore, we assess how these practices affect key metrics such as defect detection, time to market, and system stability in Linux environments, where open-source software development practices dominate. The findings highlight tha
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Ramar, Vijai Anand, Karthik Kushala, Venkataramesh Induru, Priyadarshini Radhakrishnan, and R. Lakshmana Kumar. "AI-Augmented Test Automation: Integrating Page Object Model and Behavior-Driven Development for Intelligent and Scalable Software Testing." International Journal of Multidisciplinary Research and Growth Evaluation 5, no. 2 (2024): 1078–85. https://doi.org/10.54660/.ijmrge.2024.5.2.1078-1085.

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Software testing plays a crucial role in ensuring the reliability and quality of modern applications, but traditional automation methods often struggle with scalability, maintenance, and efficiency. This research proposes an AI-Augmented Test Automation Framework that integrates the Page Object Model (POM) and Behavior-Driven Development (BDD) to enhance intelligent and scalable software testing. The framework leverages AI-driven test case generation, prioritization, and self-healing mechanisms using reinforcement learning to optimize execution time, defect detection, and maintenance costs. Pe
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Ms., Prajakta Sudhir Khade, and Rajeshkumar U. Sambhe Dr. "Artificial Intelligence in Software Development: A Review of Code Generation, Testing, Maintenance and Security." International Journal of Current Science Research and Review 08, no. 04 (2025): 1632–41. https://doi.org/10.5281/zenodo.15173328.

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Abstract : Artificial Intelligence (AI) is transforming software development by automating key processes such as code generation, testing, maintenance, and security. AI-powered tools like OpenAI Codex, GitHub Copilot, and DeepMind AlphaCode are revolutionizing programming by enhancing efficiency, reducing errors, and accelerating development cycles. Similarly, AI-driven testing frameworks improve bug detection, security analysis, and software performance optimization. This review explores recent advancements in AI-driven software development, analyzing its benefits, challenges, and ethical con
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Naharki, Kushal, Christopher Hayes, and Yong-Lak Park. "Aerial Systems for Releasing Natural Enemy Insects of Purple Loosestrife Using Drones." Drones 8, no. 11 (2024): 635. http://dx.doi.org/10.3390/drones8110635.

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Lythrum salicaria (purple loosestrife) is an invasive species that displaces native wetland flora in the USA. The detection and manual release of biological control agents for L. salicaria is challenging because L. salicaria inhabits many inaccessible areas. This study was conducted to develop aerial systems for the detection of L. salicaria and the release of its natural enemy, Galerucella calmariensis (Coleoptera: Chrysomelidae). We determined the optimal sensors and flight height for the aerial detection of L. salicaria and designed an aerial deployment method for G. calmariensis. Drone-bas
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Ali, Asif, Yuanqing Xia, Qamar Navid, et al. "Mobile-UI-Repair: a deep learning based UI smell detection technique for mobile user interface." PeerJ Computer Science 10 (May 16, 2024): e2028. http://dx.doi.org/10.7717/peerj-cs.2028.

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The graphical user interface (GUI) in mobile applications plays a crucial role in connecting users with mobile applications. GUIs often receive many UI design smells, bugs, or feature enhancement requests. The design smells include text overlap, component occlusion, blur screens, null values, and missing images. It also provides for the behavior of mobile applications during their usage. Manual testing of mobile applications (app as short in the rest of the document) is essential to ensuring app quality, especially for identifying usability and accessibility that may be missed during automated
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D., Rajya Lakshmi, and Suguna Mallika S. "A Review on Web Application Testing and its Current Research Directions." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 4 (2017): 2132–41. https://doi.org/10.11591/ijece.v7i4.pp2132-2141.

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Testing is an important part of every software development process on which companies devote considerable time and effort. The burgeoning web applications and their proliferating economic significance in the society made the area of web application testing an area of acute importance. The web applications generally tend to take faster and quicker release cycles making their testing very challenging. The main issues in testing are cost efficiency and bug detection efficiency. Coverage-based testing is the process of ensuring exercise of specific program elements. Coverage measurement helps dete
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Yoon, Jaehan, and Sooyoung Cha. "FeatMaker: Automated Feature Engineering for Search Strategy of Symbolic Execution." Proceedings of the ACM on Software Engineering 1, FSE (2024): 2447–68. http://dx.doi.org/10.1145/3660815.

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We present FeatMaker, a novel technique that automatically generates state features to enhance the search strategy of symbolic execution. Search strategies, designed to address the well-known state-explosion problem, prioritize which program states to explore. These strategies typically depend on a ”state feature” that describes a specific property of program states, using this feature to score and rank them. Recently, search strategies employing multiple state features have shown superior performance over traditional strategies that use a single, generic feature. However, the process of desig
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Yadavally, Aashish, Yi Li, Shaohua Wang, and Tien N. Nguyen. "A Learning-Based Approach to Static Program Slicing." Proceedings of the ACM on Programming Languages 8, OOPSLA1 (2024): 83–109. http://dx.doi.org/10.1145/3649814.

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Traditional program slicing techniques are crucial for early bug detection and manual/automated debugging of online code snippets. Nevertheless, their inability to handle incomplete code hinders their real-world applicability in such scenarios. To overcome these challenges, we present NS-Slicer, a novel learning-based approach that predicts static program slices for both complete and partial code Our tool leverages a pre-trained language model to exploit its understanding of fine-grained variable-statement dependencies within source code. With this knowledge, given a variable at a specific loc
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Ascari, Flavio, Roberto Bruni, Roberta Gori, and Francesco Logozzo. "Revealing Sources of (Memory) Errors via Backward Analysis." Proceedings of the ACM on Programming Languages 9, OOPSLA1 (2025): 1321–48. https://doi.org/10.1145/3720486.

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Sound over-approximation methods are effective for proving the absence of errors, but inevitably produce false alarms that can hamper programmers. In contrast, under-approximation methods focus on bug detection and are free from false alarms. In this work, we present two novel proof systems designed to locate the source of errors via backward under-approximation, namely Sufficient Incorrectness Logic (SIL) and its specialization for handling memory errors, called Separation SIL. The SIL proof system is minimal, sound and complete for Lisbon triples, enabling a detailed comparison of triple-bas
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AzraJabeen, Mohamed Ali. "Automating the Code: Exploring AI-Powered Development Tool Github Copilot." International Journal of Leading Research Publication 5, no. 10 (2024): 1–9. https://doi.org/10.5281/zenodo.14646830.

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As the demand for faster and more efficient software development grows, artificial intelligence (AI) has emerged as a transformative force in automating various aspects of coding. This paper explores the integration of AI-powered development tool Github Copilot that streamlines the coding process, enhance productivity, and assist developers in solving complex programming challenges. We examine key AI-driven technologies such as code completion, bug detection, automated refactoring, and intelligent code generation, highlighting their capabilities and applications. Additionally, we analyze the r
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Hunko, Ihor. "Optimize Mobile App Testing Using Machine Learning to Improve User Experience." Asian Journal of Research in Computer Science 18, no. 5 (2025): 403–18. https://doi.org/10.9734/ajrcos/2025/v18i5663.

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Aims: The study delves into the machine learning (ML) paradigm shift in enhancing mobile application testing processes for higher accuracy, efficiency, and overall user experience, with a particular focus on Decision Tree and Random Forest models. Study Design: Experimental Research Design. Methodology: The research applies an experimental A/B testing framework using real-world datasets and cloud-based testing environments (e.g., Firebase Test Lab) to compare ML-driven and traditional testing approaches. Techniques include automated UI defect detection through convolutional neural networks, re
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Lu, Weiqi, Yongqiang Tian, Xiaohan Zhong, et al. "An Empirical Study of Bugs in Data Visualization Libraries." Proceedings of the ACM on Software Engineering 2, FSE (2025): 2075–98. https://doi.org/10.1145/3729363.

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Data visualization (DataViz) libraries play a crucial role in presentation, data analysis, and application development, underscoring the importance of their accuracy in transforming data into visual representations. Incorrect visualizations can adversely impact user experience, distort information conveyance, and influence user perception and decision-making processes. Visual bugs in these libraries can be particularly insidious as they may not cause obvious errors like crashes, but instead mislead users of the underlying data graphically, resulting in wrong decision making. Consequently, a go
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Zhang, Junwei, Xing Hu, Shan Gao, Xin Xia, David Lo, and Shanping Li. "Less Is More: On the Importance of Data Quality for Unit Test Generation." Proceedings of the ACM on Software Engineering 2, FSE (2025): 1293–316. https://doi.org/10.1145/3715778.

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Unit testing is crucial for software development and maintenance. Effective unit testing ensures and improves software quality, but writing unit tests is time-consuming and labor-intensive. Recent studies have proposed deep learning (DL) techniques or large language models (LLMs) to automate unit test generation. These models are usually trained or fine-tuned on large-scale datasets. Despite growing awareness of the importance of data quality, there has been limited research on the quality of datasets used for test generation. To bridge this gap, we systematically examine the impact of noise o
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Niazi, Tahira, Teerath Das, Ghufran Ahmed, et al. "Investigating Novice Developers’ Code Commenting Trends Using Machine Learning Techniques." Algorithms 16, no. 1 (2023): 53. http://dx.doi.org/10.3390/a16010053.

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Code comments are considered an efficient way to document the functionality of a particular block of code. Code commenting is a common practice among developers to explain the purpose of the code in order to improve code comprehension and readability. Researchers investigated the effect of code comments on software development tasks and demonstrated the use of comments in several ways, including maintenance, reusability, bug detection, etc. Given the importance of code comments, it becomes vital for novice developers to brush up on their code commenting skills. In this study, we initially inve
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Joshi, Spandan, and Mehul Parikh. "Various License Plate Detection and Recognition Methods using Computer Vision and Machine Learning." ITM Web of Conferences 53 (2023): 02013. http://dx.doi.org/10.1051/itmconf/20235302013.

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With the increasing advancements in the technology, our lives have become significantly more convenient. We now have automated many things. One example of such things is the automated number plate recognition system. There are many ways to perform the ANPR (Automatic Number Plate Recognition). Performing ANPR in wild still remains a big challenge. This review focuses on some techniques that have tried to overcome this challenge.
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Batouta, Zouhair Ibn, Rachid Dehbi, and Mohamed Talea. "Architecture of multi-agent systems for generative automatic matching among heterogeneous systems." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 2 (2025): 2345. https://doi.org/10.11591/ijece.v15i2.pp2345-2355.

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This paper presents the generative automatic matching (GAM) approach, implemented through a multi-agent system (MAS), to address the challenges of heterogeneity across meta-models. GAM integrates automatic meta-model matching with model generation, offering a comprehensive solution to complex systems involving diverse architectures. The key innovation lies in its ability to automate both the detection of correspondences and the transformation of models, improving the precision and recall of matching processes. The system's scalability and adaptability are enhanced by MAS, allowing for efficien
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Batouta, Zouhair Ibn, Rachid Dehbi, and Mohamed Talea. "Architecture of multi-agent systems for generative automatic matching among heterogeneous systems." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 2 (2025): 2345–55. https://doi.org/10.11591/ijece.v15i2.pp2345-2355.

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This paper presents the generative automatic matching (GAM) approach, implemented through a multi-agent system (MAS), to address the challenges of heterogeneity across meta-models. GAM integrates automatic meta-model matching with model generation, offering a comprehensive solution to complex systems involving diverse architectures. The key innovation lies in its ability to automate both the detection of correspondences and the transformation of models, improving the precision and recall of matching processes. The system's scalability and adaptability are enhanced by MAS, allowing for efficien
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Zhang, Tian Hou, Chang Chun Li, and Shi Feng Wang. "Study on Edge Detection Technique in Material Bag Image." Advanced Materials Research 97-101 (March 2010): 4408–11. http://dx.doi.org/10.4028/www.scientific.net/amr.97-101.4408.

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According to the features of material bag image, the paper compares an analyzes the detection effects of different edge detection operators detecting material bag image. A new image segmentation method is proposed to combine Sobel edge detection operator and iterative threshold. The method can extract edge information of material bag image efficiently and provide a theoretical basis for the robot automatic recognition of material bags technique.
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Antonio, Gregorius Rudy. "Continuous auditing: Developing automated audit systems for fraud and error detections." Journal of Economics, Business, & Accountancy Ventura 17, no. 1 (2014): 127. http://dx.doi.org/10.14414/jebav.v17i1.272.

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Indonesian Institute of Certified Public Accountants, American Institute of Certified Public Accountants and the Canadian Institute of Chartered Accountants(SAS 99 sec 110, par 2) establishes auditors responsibility to plan and perform the audit to obtain reasonable assurance about whether the financial statements are free of material mis- statement, whether caused by error or fraud to plan and perform audits to provide a reasonable assurance that the audited financial statements are free of material fraud. This study proposed the development of Automated Audit System model to assist auditors
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Zineb, Remch, Khoulji Samira, and Kerkeb Mohamed Larbi. "Applications of smart agriculture for environmental protection using deep learning techniques." E3S Web of Conferences 412 (2023): 01083. http://dx.doi.org/10.1051/e3sconf/202341201083.

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DL, short for Deep Learning, is a cutting-edge approach that merges advanced techniques in image processing and data analysis with the power of big data analysis. Its potential is enormous and has already found practical applications in several fields, including autonomous driving, automatic speech recognition, medical research, image restoration, natural language processing, and, among others. DL has been recently introduced in agriculture showing promising results in solving various farming problems like disease detection, automated plant and fruit identification, and counting. This study pr
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Batta, Priyanka, and Miss Himanshi. "HYBRID TECHNIQUE FOR SOFTWARE CODE CLONE DETECTION." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 2, no. 2 (2012): 98–102. http://dx.doi.org/10.24297/ijct.v2i2b.2639.

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Software Clone detection is one of the hottest research area that helps in detecting duplicate code from an applications. The research has shown that 5% to 20% of software systems can contain duplicated code that is generated by simply copying the existing program code and pasting with or without minor modifications. Cloning creates problem when a bug is found in one code segment that was copied and pasted at several locations earlier. The objective of this study is to analyze the working of hybrid clone detection technique that design and analyze a hybrid technique for detecting software clon
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Siva Kumar, P. V. "Road condition assessment: A framework for automatic detection of surface flaws." BOHR International Journal of Smart Computing and Information Technology 6, no. 1 (2025): 1–11. https://doi.org/10.54646/bijscit.2025.46.

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Road abnormalities such as cracks, unevenness, potholes, and manholes are increasing the number of road disasters in today’s world, particularly in nations like India. Accidents and irreplaceable loss result from uneven and damaged roadways, as well as unneeded openings. The introduction of more Big Data sources through citizen recording devices has created a new foundation for public infrastructure management and control, as well as policy design. Roads that are maintained on a regular basis are less likely to be involved in accidents. However, manually inspecting road damage is costly, time-
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Zanatta Bruno, Gustavo, Karlla B. Chaves Rodrigues, Kleber Vieira Cardoso, Sand Luz Correa, and Cristiano Bonato Both. "Anomaly Detection in Cloud-native B5G Systems using Observability and Machine Learning COTS Solutions." Journal of Internet Services and Applications 14, no. 1 (2023): 189–99. http://dx.doi.org/10.5753/jisa.2023.3551.

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The advent of B5G networks has revolutionized the telecommunications landscape by transitioning hardware resources to software components, predominantly running on cloud­based infrastructures. However, this ‘softwarization’ extends across the radio access, transport, and core networks, introducing complex challenges in realtime network management. In this context of the ‘softwarization’, it is imperative to make the behavior of B5G systems readily observable for effective management and fault diagnosis. This article presents a comprehensive empirical investigation of observability within a B5G
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Putri, Yoanna Fransisca, Aloysius Bagas Pradipta Irianto, and Suman Sharma. "Comparison of Automatic and Manual Regression Testing on Mobile Application Health Technology with Black Box Testing Method." Indonesian Journal of Information Systems 7, no. 2 (2025): 218–30. https://doi.org/10.24002/ijis.v7i2.6850.

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The opening of opportunities for health tech services in Indonesia can lead to increased competition. This makes the company continue to innovate by updating or adding functionality to software systems that have been developed. Because of this, software testing with a shorter time is required so that test performance remains maximum. The object of this research is the Teman diabetes application developed by PT. Global Urban Essentials. In this research, the software testing conducted includes automatic regression testing and manual regression testing. The two testing approaches will be compare
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Charitidis, Polychronis, Sotirios Moschos, Archontis Pipertzis, et al. "StreetScouting: A Deep Learning Platform for Automatic Detection and Geotagging of Urban Features from Street-Level Images." Applied Sciences 13, no. 1 (2022): 266. http://dx.doi.org/10.3390/app13010266.

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Urban environments are evolving rapidly in big cities; keeping track of these changes is becoming harder. Information regarding urban features, such as the number of trees, lights, or shops in a particular region, can be crucial for tasks, such as urban planning, commercial campaigns, or inferring various social indicators. StreetScouting is a platform that aims to automate the process of detecting, visualizing, and exporting the urban features of a particular region. Recently, the advent of deep learning has revolutionized the way many computer vision tasks are tackled. In this work, we prese
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Rao, Dr T. Kameswara, Eswar Venkata Durga Rao Challa, Venkata Naidu Donapati, Sri Gayathri Pragna Gudipudi, Madhu Babu Chilaka, and Sri Ram Chandra Rao Dhavaleswarapu. "Automated Car: Intelligent Maneuver." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 1872–75. http://dx.doi.org/10.22214/ijraset.2024.59226.

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Abstract: Automated vehicles are rapidly evolving to meet the demands of modern transportation systems, aiming for enhanced safety, efficiency, and user experience. Autonomous vehicles are also a big part of these technologies. The most important action of a driver has to do is to follow the lanes on the way to the destination. This research presents Intelligent Maneuver, a sophisticated framework integrating four key functionalities crucial for automated car navigation: object avoidance, traffic light detection, self-parking, and lane detection. Leveraging advanced computer vision techniques
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Reena Chandra. "Automation Frameworks for End-to-End Testing of Large Language Models (LLMs)." Journal of Information Systems Engineering and Management 10, no. 43s (2025): 464–72. https://doi.org/10.52783/jisem.v10i43s.8400.

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Building and delivering high-quality software is critical in software engineering and requires verification and validation processes for end-to-end testing that are reliable, robust, and deliver correct results fast. Manual testing of LLM models, while feasible, is very time-consuming and inefficient and has scalability issues depending on how big the model under test (MUT) is. Recent research and cutting-edge technology innovations in LLM models have deeply influenced software engineering. We need to integrate its impact robustly in areas of model analysis, test automation, model execution, d
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Dave, B. Buragay, Emmanuel M. Escalona Lance, Ashley B. Aniag Louie, Tarhata R. Laja Putli, and Mae T. Escober Krisnelle. "The Use of Soil Moisture Sensors and Arduino Interface to Create and Automatic Watering Device for Houseplants." International Journal for Research Trends and Innovation 9, no. 4 (2024): 1099–108. https://doi.org/10.5281/zenodo.11082209.

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The COVID-19 pandemic is associated with an upturn toward home gardening as a relaxing activity, bringing a sense of peace and proximity to nature during the quarantine measures. Subsequently, after COVID-19, irregular watering patterns become a threat that houseplants face. They are poisoned by root rot from overwatering or dehydration from underwatering. To solve this problem, this study introduces an Automatic Watering Device that relies on soil moisture sensors and Arduino interfaces. The quantitative experimental research method was used to determine the accuracy of the automatic watering
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Wang, P., Y. L. Zhao, Xian Li Liu, and Yi Wen Wang. "The Key Technology Research for Vision Inspecting Instrument of Steel Ball Surface Defect." Key Engineering Materials 392-394 (October 2008): 816–20. http://dx.doi.org/10.4028/www.scientific.net/kem.392-394.816.

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It has been necessary to adopting automatic detection of steel ball based on machine vision in industrial processing. The detecting instrument for surface quality of steel ball based on machine vision and embedded control technique and is applied to detecting external bug region of steel ball in bearing. Its image detection and control system require excellent real-time character and high control accuracy. This paper put forward a new design for image processing and control system of detecting instrument. Firstly, we adopted OTSU segmentation arithmetic based on image enhancement and median fi
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