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Identifying and Involving the Real End User in software Development: Towards a Pattern Language 22

Identifying and Involving the Real End User in Software Deve...

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Proceedings of the 27th European Conference on Pattern Languages of Programs

作者： Mohammad Daud Haiderzai Valentino Vranić Institute of Informatics Information Systems and Software EngineeringFaculty of Informatics and Information Technologies Slovak University of Technology Slovakia

ISBN: (纸本)9781450395946

software development demand and identification of end users involvement have been increasing rapidly, but identifying the real end user and involving them in software development is challenging for software developers reported in the literature. One of the problems is the difficulty of designing the business domain. It takes longer to find the actual business domain when different and inexpert domain end users are engaged with developers. Another problem is the difficulty of incorporating all types of end users needs in software development. Several expectations and different information make the development process more complex and overload the project scope. software development also takes more time, and the project goal is challenging. The above problems are the leading causes that create a complex process and difficulty in software development. To overcome these problems, we established a pattern language composed of eight organizational patterns observed and documented during in practical work on many projects. These patterns and other known organizational patterns relation are described in a patterns story of a real company.

关键词： software development user involvement Organizational patterns

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A Pattern Language of Multi-Organizational Collaboration in Public Sector in Developing Countries 22

A Pattern Language of Multi-Organizational Collaboration in ...

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Proceedings of the 29th Conference on Pattern Languages of Programs

作者： Haji Gul Wahaj Valentino Vranić Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava

ISBN: (纸本)9781941652183

Researchers have extensively discussed the importance of collaborative efforts between public organizations, especially in developing countries. For outsourcing projects to be delivered successfully, collaboration is seen as a vital business trait. Due to low economy in often developing countries, several public organizations may not have access to the required resources. Therefore, sharing the resources, expertise, and lessons learned could be a good practice for governments, particularly in developing nations. Several public organizations are assigned individual tasks and responsibilities in different sectors. Collaboration with other public organizations might be facilitated by sharing infrastructure, public data, consultations, computer networks, lessons learned, and building capacity of stakeholders. Despite lack of resources, public organizations distributed across the country faced other obstacles such as inadequate infrastructure for collaboration. This study discusses a comprehensive collaboration between public organizations to share existing resources with other public organizations. During this study, we attempt to document some newly observed organizational patterns. We interlink them into a language that can assist both researchers and practitioners in understanding how to convince public organizations to collaborate.

关键词： collaboration developing countries organizational patterns patterns public sector software development software engineering

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Ppt4j: Patch Presence Test for Java Binaries 24

Ppt4j: Patch Presence Test for Java Binaries

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44th ACM/IEEE International Conference on software engineering, ICSE 2024

作者： Pan, Zhiyuan Hu, Xing Xia, Xin Zhan, Xian Lo, David Yang, Xiaohu Zhejiang University The State Key Laboratory of Blockchain and Data Security Hangzhou China Huawei Software Engineering Application Technology Lab Hangzhou China The Hong Kong Polytechnic University Hong Kong School of Computing and Information Systems Singapore Management University Singapore

ISBN: (纸本)9798400702174

The number of vulnerabilities reported in open source software has increased substantially in recent years. Security patches provide the necessary measures to protect software from attacks and vulnerabilities. In practice, it is difficult to identify whether patches have been integrated into software, especially if we only have binary files. Therefore, the ability to test whether a patch is applied to the target binary, a.k.a. patch presence test, is crucial for practitioners. However, it is challenging to obtain accurate semantic information from patches, which could lead to incorrect results. In this paper, we propose a new patch presence test framework named Ppt4j (Patch Presence Test forJava Binaries). Ppt4j is designed for open-source Java libraries. It takes Java binaries (i.e. bytecode files) as input, extracts semantic information from patches, and uses feature-based techniques to identify patch lines in the binaries. To evaluate the effectiveness of our proposed approach Ppt4j, we construct a dataset with binaries that include 110 vulnerabilities. The results show that Ppt4j achieves an F1 score of 98.5% with reasonable efficiency, improving the baseline by 14.2%. Furthermore, we conduct an in-the-wild evaluation of Ppt4j on JetBrains IntelliJ IDEA. The results suggest that a third-party library included in the software is not patched for two CVEs, and we have reported this potential security problem to the vendor. © 2024 ACM.

关键词： Open source software

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software Defect Prediction Using Ensemble Technique

Software Defect Prediction Using Ensemble Technique

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International Conference on information Management and Technology (ICIMTech)

作者： Pandu Wicaksono Panji Arisaputra Rezky Yunanda Willy Kristian Almuzhidul Mujhid Software Engineering Department School of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350390025

ISBN: (纸本)9798350390032

software defect prediction is the methodical process of identifying code segments that are likely to have problems. This is done by analyzing software metrics and using categorization algorithms. This work introduces an alternate approach that utilizes ensemble learning techniques to improve the effectiveness of fault detection models. The models utilized in this study are CodeGPT and CodeBERT, both of which are transformer-based deep learning models that can extract code features from source code. The models are trained using the PROMISE dataset, which consists of Java projects that have been labeled with defects. The test results show that using ensemble learning methods can improve the accuracy, precision, recall, and F1 score of a single model by around 1-3%. This study contributes to the progress of software dependability and quality by utilizing advanced software fault prediction algorithms.

关键词： Codes Source coding software algorithms Predictive models Prediction algorithms Feature extraction Transformers software Ensemble learning software measurement

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Building Customer Capacity Through Organizational Patterns Improves the Development team's Understanding 22

Building Customer Capacity Through Organizational Patterns I...

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Proceedings of the 29th Conference on Pattern Languages of Programs

作者： Mohammad Ismail Khattab Valentino Vranić Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava

ISBN: (纸本)9781941652183

The development team needs to work together with a customer who has a basic understanding of information technology. Customers need to be aware of the business processes and the importance of technology. A customer and the development team need to communicate and understand each other in the software project's development. software projects can be successfully built when the customer has sufficient capacity. The process of building an organization's strength and sustainability is called capacity-building. software projects are more effective and quality when capacity development takes place at the organizational level. Capacity directly affects work performance and business outcomes. Due to low customer capacity, we have found in practice and in the literature that False Requirements, No Basic Technology Knowledge, No Domain Knowledge, Mismanaged Timeline, No Trust in software Project, and Unproductive Discussion are the main customer capacity challenges to the software project development team. We documented our experiences and five newly discovered patterns in government organizations to solve the mentioned challenges. We present this documented experience in the form of patterns in this paper to improve the customer capacity Before work begins on a software project or during the software development process. Some organizational patterns that reduce the mentioned challenges are also used along with newly discovered patterns. We have illustrated the sequences of the patterns and explained how we could make a pattern language from these patterns.

关键词： customer customer capacity building development team organizational patterns patterns software projects

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Patterns of Recreating Reality in Games 22

Patterns of Recreating Reality in Games

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Proceedings of the 29th Conference on Pattern Languages of Programs

作者： Branislava Vranić Valentino Vranić Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava

ISBN: (纸本)9781941652183

There are various ways of how reality is recreated in games. These patterns recur in within a single game and throughout many games. There are contradictory forces that lead to a solution that can be reused. Clearly, these ways of recreating reality in games exhibit the properties of patterns in the Alexandrian sense, that heavily influenced the area of software development. Identifying the patterns of recreating reality in games is important in order to develop entertaining and interesting games that people will enjoy playing. Implementing them can greatly improve the quality of games. Furthermore, the patterns are flexible, thus they can be used in many creative ways. For games to be enjoyable and plausible, the reality within them needs to be recreated. Good practices in achieving this can be captured as patterns in the Alexandrian sense. This paper brings in the idea of patterns of recreating reality in games, the connections between them, how they are implemented, and how to apply them. Five patterns mined in Minecraft and other successful games are presented---The Presence of an Enjoyable Challenge, Preservation of Progress, Flexibility of the Laws of Nature, Expansion of Inventory Limits, and Fast Travel---along with the relationships between them and examples of where they can be found. A story describing gameplay of Minecraft from a player's perspective has been used to show exact examples of these patterns.

关键词： drama patterns game design game patterns laws of nature minecraft patterns

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Arabic Sentiment Analysis of Food Delivery Services Reviews

Arabic Sentiment Analysis of Food Delivery Services Reviews

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2023 International Symposium on Networks, Computers and Communications, ISNCC 2023

作者： Mustafa, Dheya Khabour, Safaa M. Shatnawi, Ahmed S. Taqieddin, Eyad The Hashemite University Faculty of Engineering Dept. of Computer Engineering Zarqa13133 Jordan Yarmouk University Faculty of Information Technology and Computer Sciences Dept. of Information Systems Irbid21163 Jordan Jordan University of Science & Technology Dept. of Software Engineering Irbid21110 Jordan School of Computing and Informatics Dept. of Cyber Security Al-Hussein Technical University Amman Jordan

ISBN: (纸本)9798350335590

Customer reviews on online platforms have grown to become an important source of insight into a company's performance. Food delivery services (FDS) companies aim to effectively use customers' feedback to identify areas for improvement of customer satisfaction. Although Arabic is becoming one of the most widely used languages on the Internet, only a few studies have focused on Arabic sentiment analysis to date. The present study conducts an extensive emotion mining and sentiment analysis on FDS-related reviews in Arabic, exploiting natural language processing, and machine learning techniques to extract information that reflects customers' subjective viewpoints, recognize their feelings, and determine polarity in the FDS domain. This work begins with collecting the FDS dataset (Talabat), and then extracting and creating a dialects lexicon for Arabic dialects, followed by walking the reader through detailed steps of cleaning and pre-processing a manually annotated dataset. Finally, we examined classification algorithms including Decision Tree (DT), and Support Vector Machine (SVM). We achieved a maximum accuracy of about 82% using the SVM classifier. © 2023 IEEE.

关键词： Sentiment analysis

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Price Estimation of Used Cars Using Machine Learning Algorithms 3rd

Price Estimation of Used Cars Using Machine Learning Algorit...

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3rd EAI International Conference on Cognitive Computing and Cyber Physical systems, IC4S 2022

作者： Valarmathi, B. Gupta, N. Srinivasa Santhi, K. Chellatamilan, T. Kavitha, A. Raahil, Armaan Padmavathy, N. Department of Software and Systems Engineering School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Analytics School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Mathematics School of Advanced Sciences Vellore Institute of Technology Tamil Nadu Vellore India Department of Information Technology School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Electronics and Communication Engineering Vishnu Institute of Technology Andhra Pradesh Bhimavaram India

ISBN: (纸本)9783031289743

In this study, machine learning (ML) techniques are employed to predict used car prices. Several features are used to calculate the price of used cars, but in this paper, we find efficient ways to find the most precise car prices. Despite the fact that there are websites offering this service, they could not employ the most precise prediction system. It is also possible to predict a used car's true market value using a variety of models and techniques. It's important to understand their genuine market value before buying or selling. Both buyers and sellers will be benefitted from these accurate predictions. Support Vector regression, Random Forest regression, and CatBoost regression techniques are used in the proposed system. In the existing method [13], mean absolute error for decision tree regression was 0.6711, which was the least among other algorithms like Linear regression, Lasso regression, Ridge regression, Bayesian Ridge regression, and etc., they used. In the proposed system, mean absolute error (MAE) for Support Vector regression, CatBoost regression and Random Forest regression techniques are 0.1459, 0.1371 and 0.1284 respectively. The prices of second hand/used cars are predicted using the CatBoost regression, Support Vector regression, and Random Forest regression techniques. The accuracy of these algorithms are 86.28%, 85.40% and 87.16%. Among these three algorithms, Random Forest regression gives the least MAE of 0.1317 and the highest accuracy of 87.16%. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Forecasting

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Smart Farmer Education System for Disease Identification, Severity Assessment and Infestation Management (Brinjal) 17

Smart Farmer Education System for Disease Identification, Se...

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17th IEEE International Conference on Industrial and information systems, ICIIS 2023

作者： Bandara, Keshara Vidhanaarachchi, Samitha Nanayakkara, A.A.E. Kulasinghe, Kavindu Rajapakshe, Sanka Wickramasinghe, Asela Yapa, Kanishka Sri Lanka Institute of Information Technology Department of Information Technology New Kandy Road Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka University of Ruhuna Faculty of Agriculture Department of Soil Science Mapalana Kamburupitiya Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350323634

The agriculture industry is one of the most significant sources of foreign exchange and employment in the Sri Lankan market. Therefore, small crops play a crucial role in ensuring the food security of the population as they are integral components of Sri Lankan cuisine. However, industry experts have identified inefficient disease and pest management as a hindrance to production. This issue requires immediate attention and proper action, which can be effectively addressed through the utilization of ICT technologies. Consequently, this research paper proposes a smart system that aims to assist farmers and agriculture professionals by providing them with farmer education, disease and infestation management tools, as well as progression level calculations. The implementation of this system aims to uplift the agriculture industry in Sri Lanka. The developed 'AI-based mobile application system for Brinjal Diseases,' which is based on analyzing the eating patterns of leafhopper damage, presents an innovative approach to identify the most significant threats to Brinjal, specifically focusing on leafhopper damage. This proposed system effectively informs various stakeholders, including the Area Agricultural Research Officer, Seed Certification and Plant Protection Center (SCPPC), Agricultural Service Center, Office of the Register of Pesticides (ROP), Plant Protection Service (PPS), as well as farmers, regarding the dispersion and prevention of brinjal diseases and pest infections. © 2023 IEEE.

关键词： Crowdsourcing

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An Effective Detection of Brain Tumor Detection Using ResNet-50 Model

An Effective Detection of Brain Tumor Detection Using ResNet...

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Advancement in Renewable Energy and Intelligent systems (AREIS), International Conference on

作者： V. Raji S. Maheswari S. Sree Dharinya P. Chinniah Kavin K. S. Kiruba Thangam Raja Dept. of Information Technology S.K.P. Engineering College Tiruvannamalai India Dept. of CSE(IoT) Nandha Engineering College Erode Tamilnadu India Dept. of Software and Systems Engineering School of Computer Science Engineering and Information Systems VIT University Vellore India Dept. of ECE ST. Joseph College of Engineering Sriperumbudur Tamilnadu India Dept. of EEE Government College of Engineering Tirunelveli Tamil Nadu India Dept. of Software and Systems Engineering School of Computer Science Engineering and Information Systems(SCORE) VIT University Vellore India

ISBN: (数字)9798350387230

ISBN: (纸本)9798350387247

Rapid proliferation of aberrant neurons in brain tumors significantly damages organ function or causes death, endangering adult health. These tumors vary widely in terms of their locations, sizes, and textures. Finding malignant tumors requires the use of Magnetic Resonance Imaging (MRI). However, physically diagnosing brain tumors is a timeconsuming and potentially unpredictable technique. Cropping and resizing to fixed dimensions ensures consistency before submitting brain MRI images for analysis using deep learning models such as modified ResNet-50. This study uses the modified ResNet-50 model, a deep Convolutional Neural Network (CNN) renowned for its accuracy and resilience in image recognition tasks, to provide an efficient method for brain tumor diagnosis. Initially trained on Image Net, the modified ResNet-50 model further improved using transfer learning on many annotated brain MRI scan datasets. By adding variation to the dataset, data augmentation makes the model more robust and helps it to generalize. Deeper networks are trained without sacrificing performance to the model's architecture, which addresses the vanishing gradient problem. This work classification model implemented using Python software, which facilitated the development, training, and evaluation processes, allowing for an analysis of their performance. The optimized model outperformed conventional Machine Learning (ML) techniques, demonstrating over 94% accuracy in tumor identification and classification tests. With the potential to enhance patient outcomes and diagnostic efficiency, the integration of modified ResNet-50 in brain tumor identification offers a significant development towards accurate and dependable automated diagnostic systems. The accuracy of the modified Resnet-50 is 94%, and the specificity comparison of the modified ResNet-50 is 93.5%, respectively.

关键词： Deep learning Analytical models Accuracy Magnetic resonance imaging Transfer learning Computer architecture Brain modeling Data augmentation Data models Convolutional neural networks

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