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International Conference on software engineering (ICSE)

作者： Feifei Niu Wesley K. G. Assunção LiGuo Huang Christoph Mayr-Dorn Jidong Ge Bin Luo Alexander Egyed State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Institute for Software Systems Engineering Johannes Kepler University Linz Austria Department of Computer Science and Engineering Southern Methodist University Dallas Texas USA

A large number of bug reports are created during the evolution of a software system. Locating the source code files that need to be changed in order to fix these bugs is a challenging task. Information retrieval-based bug localization techniques do so by correlating bug reports with historical information about the source code (e.g., previously resolved bug reports, commit logs). These techniques have shown to be efficient and easy to use. However, one flaw that is nearly omnipresent in all these techniques is that they ignore code refactorings. Code refactorings are common during software system evolution, but from the perspective of typical version control systems, they break the code history. For example, a class when renamed then appears as two separate classes with separate histories. Obviously, this is a problem that affects any technique that leverages code history. This paper proposes a refactoring-aware traceability model to keep track of the code evolution history. With this model, we reconstruct the code history by analyzing the impact of code refactorings to correctly stitch together what would otherwise be a fragmented history. To demonstrate that a refactoring aware history is indeed beneficial, we investigated three widely adopted bug localization techniques that make use of code history, which are important components in existing approaches. Our evaluation on 11 open source projects shows that taking code refactorings into account significantly improves the results of these bug localization techniques without significant changes to the techniques themselves. The more refactorings are used in a project, the stronger the benefit we observed. Based on our findings, we believe that much of the state of the art leveraging code history should benefit from our work.

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Towards a Smart Home : An Intelligent Approach to Environment Monitoring and Anti-Theft Alarming

Towards a Smart Home : An Intelligent Approach to Environmen...

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International Conference on Awareness Science and Technology (iCAST)

作者： Ravitha Wathsala Malith Silva Ravindu Kodikara Sadev Ekanayake Narmada Gamage Poojani Gunathilake Department of Computer Systems Engineering Sri Lanka Institute of Information Technology (SLIIT) Malabe Sri Lanka Department of Computer Science & Software Engineering Sri Lanka Institute of Information Technology (SLIIT) Malabe Sri Lanka

Recently Smart Home concept has been a popular choice as a solution for emerging security related problems. The primary objective of this research was to create a cyber-threat free fully functioning smart home monitoring and anti-theft alarming system with enhanced physical security mechanisms. The focus of this research was to create a holistic and secure smart home system, combining cutting-edge physical security measures. The study introduced novel Intruder Access Prevention methods rooted in human behavior and voice pattern recognition, while also incorporating blockchain and network traffic analysis to safeguard the homeowner's data. Furthermore, a pioneering voice-controlled monitoring mechanism, utilizing protective energy-saving plug technology, was devised to enhance safety within contemporary households. The human behavior recognition and voice recognition-based intruder access prevention system demonstrated over 80% accuracy in intruder prevention, while user data protection mechanism prevents the communication channel from cyber hackings. Further, the smart plug demonstrates reliable and accurate physical environment monitoring with minimum latency. These results underscore the system's significant contribution to home security, marking a noteworthy advancement in the Smart Home concept.

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A New Application of Machine Learning: Detecting Errors in Network Simulations 22

A New Application of Machine Learning: Detecting Errors in N...

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Proceedings of the Winter Simulation Conference

作者： Maciej K. Wozniak Luke Liang Hieu Phan Philippe J. Giabbanelli Department of Intelligent Systems KTH Royal Institute of Technology Stockholm SWEDEN Department of Computer Science & Software Engineering Miami University Oxford OH USA

After designing a simulation and running it locally on a small network instance, the implementation can be scaled-up via parallel and distributed computing (e.g., a cluster) to cope with massive networks. However, implementation changes can create errors (e.g., parallelism errors), which are difficult to identify since the aggregate behavior of an incorrect implementation of a stochastic network simulation can fall within the distributions expected from correct implementations. In this paper, we propose the first approach that applies machine learning to traces of network simulations to detect errors. Our technique transforms simulation traces into images by reordering the network's adjacency matrix, and then training supervised machine learning models. Our evaluation on three simulation models shows that we can easily detect previously encountered types of errors and even confidently detect new errors. This work opens up numerous opportunities by examining other simulation models, representations (i.e., matrix reordering algorithms), or machine learning techniques.

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Location Intelligence Based Smart E-Commerce Platform for Residential Real-Estate Industry 3

Location Intelligence Based Smart E-Commerce Platform for Re...

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3rd International Conference on Smart Electronics and Communication, ICOSEC 2022

作者： Balasooriya, Tharindu Kavishka, Lakindu Dananjana, Isini Yumna, Maryam Thelijjagoda, Samantha Attanayaka, Buddhima Marasinghe, Lanil Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Sliit Business School Malabe Sri Lanka Department of Computer Systems Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Sysco Labs Colombo Sri Lanka

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Making the decision to purchase or invest in real estate can be a very crucial process due to its high financial risk. The purchasing decision of residential real estate properties can be even more decisive because, apart from the financial risk, the choice of a property can have a great impact on the future lifestyle of the buyer. When considering residential real estate, one major factor to be considered is the property location. This research sought to determine the applicability of modern technologies such as location intelligence and machine learning in the development of an e-commerce system that may assist users in making optimal residential real estate location decisions. Third-party web APIs were used to obtain location data, and as a result of the study, methodologies were defined to convert location data into meaningful insights by using statistical methods like weighted sum and the analytical hierarchy process. The functionalities in the proposed system have been designed, considering the roles of both buyers and sellers in the real estate business. In the proposed system, the location quality index framework provides overall insights on the location, the personalized insights and alternative locations are generated via the personal preference-based suitability analyzer and the price prediction system provides the current and future price fluctuations. The usefulness of image processing technologies and machine learning for making the sellers' journey easier on a real estate platform has also been assessed. © 2022 IEEE.

关键词： Decision making

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The ABLoTS Approach for Bug Localization: is it replicable and generalizable?

The ABLoTS Approach for Bug Localization: is it replicable a...

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International Mining software Repositories, MSR

作者： Feifei Niu Christoph Mayr-Dorn Wesley K. G. Assunção LiGuo Huang Jidong Ge Bin Luo Alexander Egyed State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Institute for Software Systems Engineering Johannes Kepler University Linz Austria Department of Computer Science and Engineering Southern Methodist University Dallas Texas USA

Bug localization is the task of recommending source code locations (typically files) that probably contain the cause of a bug and hence need to be changed to fix the bug. Along these lines, information retrieval-based bug localization (IRBL) approaches have been adopted, which identify the most bug-prone files from the source code space. In current practice, a series of state-of-the-art IRBL techniques leverage the combination of different components, e.g., similar reports, version history, code structure, to achieve better performance. ABLoTS is a recently proposed approach with the core component, TraceScore, that utilizes requirements and traceability information between different issue reports, i.e., feature requests and bug reports, to identify buggy source code snippets with promising results. To evaluate the accuracy of these results and obtain additional insights into the practical applicability of ABLoTS, supporting of future more efficient and rapid replication and comparison, we conducted a replication study of this approach with the original data set and also on an extended data set. The extended data set includes 16 more projects comprising 25,893 bug reports and corresponding source code commits. While we find that the TraceScore component as the core of ABLoTS produces comparable results with the extended data set, we also find that the ABLoTS approach no longer achieves promising results, due to an overlooked side effect of incorrectly choosing a cut-off date that led to training data leaking into test data with significant effects on performance.

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Suggestion Mining from Opinionated Text of Big Social Media Data

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computers, Materials & Continua 2021年第9期68卷 3323-3338页

作者： Youseef Alotaibi Muhammad Noman Malik Huma Hayat Khan Anab Batool Saif ul Islam Abdulmajeed Alsufyani Saleh Alghamdi Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Faculty of Engineering and Computer SciencesNational University of Modern LanguagesIslamabadPakistan Department of Software Engineering Faculty of Engineering and Computer SciencesNational University of Modern LanguagesIslamabadPakistan Department of Computer Sciences Institute of Space TechnologyIslamabadPakistan Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia

:Social media data are rapidly increasing and constitute a source of user opinions and tips on a wide range of products and *** increasing availability of such big data on biased reviews and blogs creates challenges for customers and businesses in reviewing all content in their decision-making *** overcome this challenge,extracting suggestions from opinionated text is a possible *** this study,the characteristics of suggestions are analyzed and a suggestion mining extraction process is presented for classifying suggestive sentences from online customers’reviews.A classification using a word-embedding approach is used via the XGBoost *** two datasets used in this experiment relate to online hotel reviews and Microsoft Windows App Studio discussion reviews.F1,precision,recall,and accuracy scores are *** results demonstrated that the XGBoost classifier outperforms—with an accuracy of more than 80%.Moreover,the results revealed that suggestion keywords and phrases are the predominant features for suggestion ***,this study contributes to knowledge and practice by comparing feature extraction classifiers and identifying XGBoost as a better suggestion mining process for identifying online reviews.

关键词： Suggestion mining word embedding Naïve Bayes random forest XGBoost dataset

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SID: Service Identification and Discovery Framework for Decentralised Supply Chains

SID: Service Identification and Discovery Framework for Dece...

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software Services engineering (SSE) IEEE International Conference on

作者： Hang Zhao Helen Paik Qin Wang Shining Chen School of Computer Science and Engineering The University of New South Wales Sydney Australia Software Systems Group CSIRO Data61 Sydney Australia

The traditional decentralised supply chains are facing challenges to meet the increasing demands for openness, transparency, trust and efficiency. As a result, blockchain-based decentralised supply chains are emerging. However, how to identify and discover various services in a decentralised supply chain remains an unsolved issue. This paper presents a new framework for modelling and discovering services in decentralised supply chain systems. In the framework, W3C Decentralised Identifier (DID) is adopted to describe service attributes to meet different business and technical requirements for a supply chain service. While blockchain is used for publishing and storing DID, a graphic-database-based service repository is proposed to organize these DID strings in a natural way as a graph for better service discovery. An Ethereum-based prototype is implemented as a proof of concept to demonstrate its feasibility and usefulness.

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Heuristic Search for Nonlinear Substitutions for Cryptographic Applications

Lecture Notes on Data Engineering and Communications Technol...

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Lecture Notes on Data engineering and Communications Technologies 2023年 180卷 288-298页

作者： Kuznetsov, Oleksandr Frontoni, Emanuele Kandiy, Sergey Smirnov, Oleksii Ulianovska, Yuliia Kobylianska, Olena Department of Political Sciences Communication and International Relations University of Macerata Via Crescimbeni 30/32 Macerata62100 Italy Department of Information and Communication Systems Security Faculty of Comupter Science V. N. Karazin Kharkiv National University 4 Svobody Sq Kharkiv61022 Ukraine Department of Information Engineering Marche Polytechnic University Via Brecce Bianche 12 Ancona60131 Italy Cybersecurity & Software Academic Department Central Ukrainian National Technical University 8 University Avenue Kropyvnytskyi25006 Ukraine Department of Computer Science and Software Engineering University of Customs and Finance Vernadskogo Street 2/4 Dnipro49000 Ukraine

Heuristic algorithms are used to solve complex computational problems quickly in various computer applications. Such algorithms use heuristic functions that rank the search alternatives instead of a full enumeration of possible variants. The algorithm selects, at each iteration, an alternative with the best value for the heuristics. In this paper, we investigate the complex computational problem of finding highly nonlinear substitutions (S-boxes) in the space of 8-bit permutations. The generation of S-boxes is an important field of research, since nonlinear substitutions are widely used in various cryptographic applications. For instance, S-boxes in symmetric ciphers are responsible for cryptographic strength to linear, differential, algebraic, and other types of cryptanalysis. We propose new heuristics in the form of a cost function, calculated with the Walsh-Hadamard transform. We use the Hill Climbing Algorithm to find highly nonlinear substitutions. Our experiments demonstrate that the new heuristics give good results – it takes about 80,000 iterations of the algorithm to generate 8-bit S-boxes with a nonlinearity of 104. For the optimized parameters, the number of iterations of the algorithm is comparable to the best known results, which confirms the significance and value of the research. © The Author(s).

关键词： Cost functions

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Modeling and Real-Time Simulation of Microgrid Components Using Systemc-Ams

Modeling and Real-Time Simulation of Microgrid Components Us...

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Simulation Winter Conference

作者： Rahul Bhadani Gabor Karsai Hao Tu Srdjan Lukic Institute for Software Integrated Systems Vanderbilt University Nashville TN USA Electrical and Computer Engineering North Carolina State University Raleigh NC USA

Microgrids are localized power systems that can function independently or alongside the main grid. They consist of interconnected generators, energy storage, and loads that can be managed locally. Using SystemC-AMS, we demonstrate how microgrid components, including solar panels and converters, can be accurately modeled and simulated, along with their interactions. Real-time simulations are crucial for understanding microgrid behavior and optimizing components. This approach facilitates seamless integration with hardware prototypes and automation systems, supporting various development stages. Our study presents a best-case scenario for real-time simulation, assuming each loop takes less time than the simulation time step, with fallback to the previous value if data isn’t received in time. This article introduces the first known real-time simulation strategy using SystemC-AMS, enabling the real-time simulation of microgrid components and integration with external devices. The implementation adopts a model-based design approach, creating increasingly complex systems with grid components and controllers.

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Enhancing Intrusion Detection systems Using RNN, LSTM, and Hybrid RNN-LSTM Models 2

Enhancing Intrusion Detection Systems Using RNN, LSTM, and H...

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2nd International Conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Ebadinezhad, Sahar Nia, Nooshin Nooraei Shirzad, Nasratullah Osemeha, Nwabueze Kenneth Near East University Department of Computer Information Systems Nicosia99138 Cyprus Artificial Intelligence Near East University Nicosia Cyprus Near East University Software Engineering Department Nicosia Cyprus

ISBN: (纸本)9798331505745

Due to The lack of comparison studies and practical applications of RNN, LSTM, and hybrid RNN-LSTM models for intrusion detection systems, especially when managing class imbalances in complex network datasets, represents a gap in the literature. This study investigates the application of deep learning techniques to improve the detection capabilities of IDS. For this study, we applied and assessed Recurrent Neural Network, Long Short-Term Memory, and Hybrid RNN-LSTM models on a UNSW-NB15 dataset, and to redress class imbalance we trained and validated our models using synthetic minority over-sampling. The RNN-LSTM model was shown to perform the best with a 9 4. 0 0 % accuracy in comparison to other models. These experimental results signify the possibility of enhancing IDS performance and a dependable method of detecting various network intrusions with the use of hybrid models comprised of RNN & LSTM. © 2025 IEEE.

关键词： Recurrent neural networks

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