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Systematic Review of Machine Learning-Based Open-Source software Maintenance Effort Estimation

Recent Advances in Computer Science and Communications 2023年第3期16卷 27-51页

作者： Miloudi, Chaymae Cheikhi, Laila Abran, Alain Software Project Management Team ENSIAS Mohammed V. University in Rabat Rabat Morocco Department of Software Engineering & Information Technology École de Technologie Supérieure Montréal Canada

Background: software maintenance is known as a laborious activity in the software lifecycle and is often considered more expensive than other activities. Open-Source software (OSS) has gained considerable acceptance in the industry recently, and the Maintenance Effort Estimation (MEE) of such software has emerged as an important research topic. In this context, researchers have conducted a number of open-source software maintenance effort estimation (O-MEE) studies based on statistical as well as machine learning techniques for better estimation. Objective: The objective of this study is to perform a systematic literature review (SLR) to analyze and summarize the empirical evidence of O-MEE ML techniques in current research through a set of five Research Questions (RQs) related to several criteria (e.g. data pre-processing tasks, data mining tasks, tuning parameter methods, accuracy criteria and statistical tests, as well as ML techniques reported in the literature that outperformed). Methods: We performed a systematic literature review of 36 primary empirical studies published from 2000 to June 2020, selected based on an automated search of six digital databases. Results: The findings show that Bayesian networks, decision tree, support vector machines and instance-based reasoning were the ML techniques most used;few studies opted for ensemble or hybrid techniques. Researchers have paid less attention to O-MEE data pre-processing in terms of feature selection, methods that handle missing values and imbalanced datasets, and tuning parameters of ML techniques. Classification data mining is the task most addressed using different accuracy criteria such as Precision, Recall, and Accuracy, as well as Wilcoxon and Mann-Whitney statistical tests. Conclusion: This SLR identifies a number of gaps in the current research and suggests areas for further investigation. For instance, since OSS includes different data source formats, researchers should pay more attention to data p

关键词： Open source software

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Data Owner Benefit-Driven Design of People Analytics

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Proceedings of the ACM on Human-Computer Interaction 2023年第EICS期7卷 1-38页

作者： Zander, Patrik Zieglmeier, Valentin Technical University of Munich TUMSchool of Computation Information and Technology Chair of Software and Systems Engineering Boltzmannstr. 3 Garching85748 Germany

With increasingly digitalized workplaces, the potential for sophisticated analyses of employee data rises. This increases the relevance of people analytics (PA), which are tools for the behavioral analysis of employees. Despite this potential, the successful usage of PA is hindered by employee concerns. Especially in Europe, where the GDPR or equivalent laws apply, employee consent is required before data can be processed in PA. Therefore, PA can only provide relevant insights if employees are willing to share their data. One potential way of achieving this is the use of appeal strategies. In the design of PA, the core strategy that can be used is the inclusion of data owner benefits, such as automated feedback, that are given to employees in exchange for sharing their own data. In this paper, we examine benefits as an appeal strategy and develop four design principles for the inclusion of benefits in PA. Then, we describe an exemplary set of analyses and benefits, demonstrating how our principles may be put into practice. Based on this exemplary implementation, we describe and discuss the results of a user study (n = 46) among employees in the EU and UK. Our study investigates the factors that foster or hinder employees' consent to sharing their data with PA. Then, we introduce our data owner benefits and analyze whether they can positively influence this consent decision. Our introduced data owner benefits were, contrary to our expectations, not suited to motivate our participants to consent to sharing their data. We therefore analyze how participants judge the benefits. Participants generally appreciate having them, confirming the value of including data owner benefits when designing PA. Some of our introduced benefits negatively influenced participants' sharing decision, though, meaning that careful consideration of potential risks is required when conceptualizing them. © 2023 ACM.

关键词： Personnel

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A New Model Indicator to Trade Foreign Exchange Market

A New Model Indicator to Trade Foreign Exchange Market

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International Conference on Advanced Intelligent systems for Sustainable Development, AI2SD 2022

作者： Ahouat, Chaimae El Bouchti, Karim Reda, Oussama Mohamed Nadi, Oumaima Ziti, Soumia Rabat Morocco Algorithms Networks Intelligent Systems and Software Engineering Research Team Rabat Morocco

ISBN: (纸本)9783031263835

The Foreign Exchange (FOREX) market is a financial market with a daily volume of around $6.6 trillion. The importance of Forex as the largest market in the world, traders need to have a trading plan to make profits and be a successful trader. A forex trading strategy defines a system that a forex trader uses to determine when to buy or sell a currency pair. There are various forex strategies that traders can use including technical analysis or fundamental analysis. A good forex trading strategy allows for a trader to analyze the market and confidently execute trades with sound risk management techniques. In this paper, we propose a Foreign Exchange strategy for currency markets based on a combination of two technical indicators: Relative Strength Index and Triangular Moving Average Bands as well as the price value. The combination of these latters are ensured by a specific rules when buying and selling positions. The proposed model provides a good number of sell and buy signals depending on the model inputs. We have used MetaTrader 4 to implement our model which it makes easy to automate rules and indicators combinations. In addition, this let traders backtest trading strategies to see how they would have performed in the past. The implemented results showed that the proposed model provides an interesting result. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Sales

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Machine Learning-Based Detection of Unmanned Aerial Vehicles Using Radio Frequency Signals

Machine Learning-Based Detection of Unmanned Aerial Vehicles...

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2024 Innovations in Intelligent systems and Applications Conference, ASYU 2024

作者： Okkalı, Emel Şolpan, Şevval Küçük, Kerem Information Systems Enginneering Kocaeli University Kocaeli Turkey Software Engineering Kocaeli University Kocaeli Turkey

ISBN: (纸本)9798350379433

In recent years, Unmanned Aerial Vehicles (UAVs), commonly known as drones, have rapidly gained popularity due to technological advancements and significant cost reductions. Although UAVs have demonstrated their effectiveness in a wide range of applications, from security to agriculture, and from research to rescue operations, they are also being used for malicious activities, leading to significant concerns regarding privacy, security, and safety. This study employs low-complexity machine learning methods to detect UAVs by using Radio Frequency (RF) signals emitted during real-time communication between the UAV and the control device, as opposed to deep learning methods. For this purpose, the performance of K-Nearest Neighbors (KNN), Random Forest (RF), and Decision Tree (CART) algorithms was evaluated. The results show that these algorithms are effective for UAV detection with an accuracy and F1 score exceeding 99.7%, and for UAV identification with an accuracy and F1 score of 88.4%. © 2024 IEEE.

关键词： Deep learning

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Rethinking People Analytics With Inverse Transparency by Design

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arXiv 2023年第CSCW2期7卷 1-29页

作者： Zieglmeier, Valentin Pretschner, Alexander Technical University of Munich TUM School of Computation Information and Technology Software and Systems Engineering Boltzmannstr. 3 Garching85748 Germany

Employees work in increasingly digital environments that enable advanced analytics. Yet, they lack oversight over the systems that process their data. That means that potential analysis errors or hidden biases are hard to uncover. Recent data protection legislation tries to tackle these issues, but it is inadequate. It does not prevent data misusage while at the same time stifling sensible use cases for data. We think the conflict between data protection and increasingly data-driven systems should be solved differently. When access to an employees’ data is given, all usages should be made transparent to them, according to the concept of inverse transparency. This allows individuals to benefit from sensible data usage while addressing the potentially harmful consequences of data misusage. To accomplish this, we propose a new design approach for workforce analytics software we refer to as inverse transparency by design. To understand the developer and user perspectives on the proposal, we conduct two exploratory studies with students. First, we let small teams of developers implement analytics tools with inverse transparency by design to uncover how they judge the approach and how it materializes in their developed tools. We find that architectural changes are made without inhibiting core functionality. The developers consider our approach valuable and technically feasible. Second, we conduct a user study over three months to let participants experience the provided inverse transparency and reflect on their experience. The study models a software development workplace where most work processes are already digital. Participants perceive the transparency as beneficial and feel empowered by it. They unanimously agree that it would be an improvement for the workplace. We conclude that inverse transparency by design is a promising approach to realize accepted and responsible people analytics. Copyright © 2023, The Authors. All rights reserved.

关键词： Transparency

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Enhancing Arabic Cyberbullying Detection with End-to-End Transformer Model

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Computer Modeling in engineering & Sciences 2024年第11期141卷 1651-1671页

作者： Mohamed A.Mahdi Suliman Mohamed Fati Mohamed A.G.Hazber Shahanawaj Ahamad Sawsan A.Saad Information and Computer Science Department College of Computer Science and EngineeringUniversity of Ha’ilHa’il55476Saudi Arabia Information Systems Department College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Software Engineering Department College of Computer Science and EngineeringUniversity of Ha’ilHa’il55476Saudi Arabia Computer Engineering Department College of Computer Science and EngineeringUniversity of Ha’ilHa’il55476Saudi Arabia

Cyberbullying,a critical concern for digital safety,necessitates effective linguistic analysis tools that can navigate the complexities of language use in online *** tackle this challenge,our study introduces a new approach employing Bidirectional Encoder Representations from the Transformers(BERT)base model(cased),originally pretrained in *** model is uniquely adapted to recognize the intricate nuances of Arabic online communication,a key aspect often overlooked in conventional cyberbullying detection *** model is an end-to-end solution that has been fine-tuned on a diverse dataset of Arabic social media(SM)tweets showing a notable increase in detection accuracy and sensitivity compared to existing *** results on a diverse Arabic dataset collected from the‘X platform’demonstrate a notable increase in detection accuracy and sensitivity compared to existing methods.E-BERT shows a substantial improvement in performance,evidenced by an accuracy of 98.45%,precision of 99.17%,recall of 99.10%,and an F1 score of 99.14%.The proposed E-BERT not only addresses a critical gap in cyberbullying detection in Arabic online forums but also sets a precedent for applying cross-lingual pretrained models in regional language applications,offering a scalable and effective framework for enhancing online safety across Arabic-speaking communities.

关键词： Cyberbullying offensive detection Bidirectional Encoder Representations from the Transformers(BERT) continuous bag of words Social Media natural language processing

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Visualization of Professional Cyclists Analytics

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SN Computer Science 2025年第3期6卷 1-6页

作者： Rize, Denis Sinai, Perry Holohan, Liam Saldanha, Paulo Moskovitch, Robert Software and Information Systems Engineering Ben Gurion University of the Negev Beer Sheva Israel Department of Kinesiology McGill University Montreal Canada

Cycling is an important field of sport and a great example of a sport in which athletes are highly measured due to cycling computers that monitor and document workouts in detail. Leveraging this variety of data, we developed The Velodrome, a web-based analytics tool in collaboration with the Israel Premier Tech pro-cycling team to support decision-making. Unlike traditional tools that focus on individual cyclists, The Velodrome enables comparative analysis of multiple cyclists, assisting coaches and directeur sportifs in race selection, strategic preparation, and training decisions. The Velodrome integrates both objective metrics (e.g., relative power, elevation gain) and subjective metrics (e.g., sleep quality, fatigue level) to provide a holistic view of each cyclist’s physical and mental state. The platform offers various visualizations, including radar and line charts, facilitating multi-cyclist and time-based comparisons. These features enable detailed insights into training loads, performance trends, and readiness for competition, supporting team-level decision-making. © The Author(s) 2025.

关键词： Coaching support Cycling Data visualization Sports analytics

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Reliable Task Offloading for 6G-Based IoT Applications

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Computers, Materials & Continua 2025年第2期82卷 2255-2274页

作者： Usman Mahmood Malik Muhammad Awais Javed Ahmad Naseem Alvi Mohammed Alkhathami Department of Computer Software Engineering National University of Science and Technology(NUST)Islamabad44000Pakistan Department of Electrical and Computer Engineering COMSATS University IslamabadIslamabad45550Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

Fog computing is a key enabling technology of 6G systems as it provides quick and reliable computing,and data storage services which are required for several 6G *** Intelligence(AI)algorithms will be an integral part of 6G systems and efficient task offloading techniques using fog computing will improve their performance and *** this paper,the focus is on the scenario of Partial Offloading of a Task to Multiple Helpers(POMH)in which larger tasks are divided into smaller subtasks and processed in parallel,hence expediting task ***,using POMH presents challenges such as breaking tasks into subtasks and scaling these subtasks based on many interdependent factors to ensure that all subtasks of a task finish simultaneously,preventing resource ***,applying matching theory to POMH scenarios results in dynamic preference profiles of helping devices due to changing subtask sizes,resulting in a difficult-to-solve,externalities *** paper introduces a novel many-to-one matching-based algorithm,designed to address the externalities problem and optimize resource allocation within POMH ***,we propose a new time-efficient preference profiling technique that further enhances time optimization in POMH *** performance of the proposed technique is thoroughly evaluated in comparison to alternate baseline schemes,revealing many advantages of the proposed *** simulation findings indisputably show that the proposed matching-based offloading technique outperforms existing methodologies in the literature,yielding a remarkable 52 reduction in task latency,particularly under high workloads.

关键词： 6G IoT task offloading fog computing

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A Comparison of Deep Learning and Machine Learning Methods for Water Quality Prediction 3

A Comparison of Deep Learning and Machine Learning Methods f...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Srinivasa Gupta, N. Valarmathi, B. Hiwanj, Ishaan Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Information Technology School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798331527549

Predicting water quality is essential to preserving human health and environmental sustainability. Traditional water quality assessment methods often face scalability and real-time monitoring limitations. With accuracies of 62%, 72%, 83%, 69%, 63%, 66%, 71%, 63%, and 64%, respectively, the current techniques utilized were Logistic Regression, Decision Trees, Random Forest Regressor, Extreme Gradient Boosting, Naive Bayes, K-nearest neighbors, Support Vector Machine, AdaBoost, and Bagging [9]. This study addresses these challenges by leveraging Adaptive Synthetic Sampling (ADASYN) to balance the dataset and evaluating model performance on datasets of 5,000 and 10,000 entries per class. A robust dataset obtained from Kaggle was used, with five models – Long Short-Term Memory (LSTM), Feed Forward Neural Network (FFNN), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Random Forest – evaluated and compared. The proposed methods demonstrate significant improvements in accuracy, with XGBoost achieving the highest accuracy of 95.53%, followed by Random Forest at 93.98%. This work underscores the importance of advanced machine learning techniques in addressing the limitations of traditional methods, enhancing accuracy, scalability, and adaptability in water quality prediction. These findings contribute to advancing environmental monitoring and management practices with reliable, data-driven insights. © 2025 IEEE.

关键词： Support vector machines

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Leveraging Geographic information systems and Remote Sensing for Enhanced Water Resource Management in Kazakhstan 4

Leveraging Geographic Information Systems and Remote Sensing...

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4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Hamada, Mohamed Ahmed Tolkyn, Orden Hamada, Gehad M. International It University Information Systems Department Almaty Kazakhstan International It University Computer Systems and Software Engineering Department Almaty Kazakhstan

ISBN: (纸本)9798350387490

Kazakhstan's deputy member is vast, and is one of sparse population, scattering and creating an overwhelming challenge to map and assess the water resources. Most of the waters are in remote and poorly equipped areas, making their monitoring by conventional means arduous. This includes costly and inefficient monitoring that results in a blurred condition of the water resources. Such a predicament stands in the way of effective management and decision-making regarding water resources. Therefore, we used Sentinel-3 satellite images to assess the spatiotemporal dynamics of water resources in Kazakhstan and obtained the results through GIS analysis. The main case of the region for case studies reflecting problems in the management of water resources in the country is the northern part of the Aral Sea. It presents a study characterized by in-depth data analysis and mapping that provided insight into the spatial trends of water resources, their variability, and potential areas of concern. The present research found that GIS and remote sensing technologies have emerged as a strong water resource observation platform. They show promise in the resource sector decision support. The development thus justifies the integration of these technology tools within the water resource management practices to ensure sustainable water use in Kazakhstan. The publication has equally availed a model for similarly water-stressed regions regarding technology-driven solutions in matters of environmental sustainability and resource management. © 2024 IEEE.

关键词： information management

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