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Enhancing Extreme Programming (XP) Adoption through SAMAM: A Scalable Agile Maturity Assessment Model Based on Industry Best Practices

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IEEE Access 2025年 13卷 97647-97680页

作者： Abrar, Muhammad Faisal Alferaidi, Ali Almurayziq, Tariq S. Saqib, Muhammad Khan, Wilayat Khan, Zawar Alsaffar, Mohammad University of Ha’il College of Computer Science and Engineering 55476 Saudi Arabia University of Engineering & Technology Department of Software Engineering Peshawar 25000 Pakistan

The adoption of Extreme Programming (XP), a widely recognized Agile methodology, faces numerous barriers that hinder its successful implementation in software development organizations. This research aims to develop a novel Scalable Agile Maturity Assessment Model (SAMAM) to address these barriers and facilitate the effective adoption of XP. The model is designed by leveraging established frameworks, including the Capability Maturity Model Integration (CMMI), software Outsourcing Vendor Readiness Model (SOVRM), and software Process Improvement Implementation Management Model (SPIIMM). Unlike traditional models that rely on predefined Key Process Areas (KPAs), SAMAM adopts 14 critical barriers (CBs) identified through a Systematic Literature Review (SLR) and corresponding practices as the foundation for its maturity levels. The study was conducted in four phases. First, an SLR was performed to identify 14 critical barriers to XP adoption and their respective mitigation practices. In the second phase, a survey questionnaire was administered within the software industry to validate the SLR findings and extract additional industry-relevant practices. The third phase involved the development of SAMAM, structured into five maturity levels using the identified barriers and practices instead of traditional KPAs. In the final phase, industrial case studies were conducted to evaluate the model’s effectiveness in real-world settings using the Motorola Assessment Tool. The findings demonstrate that SAMAM provides a comprehensive and scalable approach to assess and improve XP adoption maturity by systematically addressing critical adoption barriers. The model supports organizations in overcoming XP adoption challenges and achieving higher process maturity. The evaluation through case studies confirmed the practical applicability and effectiveness of the proposed model, contributing to the body of knowledge on agile methodologies and advancing XP adoption in the software developm

关键词： Agile maturity assessment mode Extreme Programming (XP) Adoption Scalable agile adoption (SAMAM) software process improvement

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An Efficient Classification Techniques for Brain Tumor Using Features Extraction and Statistic Methods, with Machine Learning Algorithms

An Efficient Classification Techniques for Brain Tumor Using...

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作者： Badshah, Shah Hussain Farhatullah khan, Gul Zaman Hassan, Muhammad Abul Junaid, Hazrat Sohail, Muhammad Mahbob, Muhammad Awais Ahamad, Izaz Ullah, Nadeem Department of Computing and Technology Abasyn University Peshawar Pakistan School of Automation China University of Geosciences Wuhan430074 China Department of Computer Software Engineering University of Engineering and Technology Mardan Pakistan Department of Computer Science and Information Technology University of Malakand Dir Lower Pakistan Department of Computer Science Bahria University Islamabad Pakistan Department of Computer Science University of Engineering and Technology Taxila Pakistan Department of Information Engineering and Computer Science University of Trento Trento38122 Italy

Today, brain tumor is a very dangerous disease that can even cause death. There are many ways to classify Brain MRI images of tumors. Various aspects of current research have limitations;some methods are accurate but take a long time to compute while some algorithms are fast, but with low accuracy. Consequently, a lot of effort is needed in this area. In this paper, our major goal is to reduce computation time and improve the classification accuracy for brain MRI images. This research proposes four phases: preprocessing, feature extraction, feature selection, and classification. During pre-processing, the median filter was used to reduce noise in the images, and the grayscale image size was reduced to further limit the image for efficient use. After that the Gray images are then given as inputs to the Feature extraction phase to select a limited feature from the image namely, texture features, shape features, smoothing features. The images features obtained in the feature’s extraction phase are still large, and we cannot simply feed them to machine learning models for processing due to computational constraints. So, we need to analyze it and pick out some interesting elements from the images. The statistical features namely, variances, mean, correlation are used in the feature selection phase to select more significant features to reduce the size of the features and reduce computational time. These features are combined and labeled in a file to train Machine Learning (ML) algorithms. We used two types of machine learning algorithms in the classification phase: classifier and evolutionary algorithm. The selected classifier algorithms results are combined in a file and we named it S-K-E-L Classifier and it gives us acceptable results in terms of accuracy and time. Similarly, an evolutionary algorithm named P-S-D-G Classifier also gives us acceptable results in terms of accuracy and time. Lastly, we combine both S-K-E-L Classifier and P-S-D-G Classifier in a file with

关键词： Classification (of information)

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Online Sexism Detection and Classification by Injecting User Gender Information

Online Sexism Detection and Classification by Injecting User...

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2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2023

作者： Das, Amit Rahgouy, Mostafa Zhang, Zheng Bhattacharya, Tathagata Dozier, Gerry Seals, Cheryl D. Auburn University Department of Computer Science and Software Engineering AuburnAL United States Auburn University at Montgomery Department of Computer Science MontgomeryAL United States

ISBN: (纸本)9798350322347

This paper investigates the potential effects that user gender information has on online sexism detection, in terms of both binary and multi class detection. Social media has recently developed into a center for sexist posts that target especially women. Since most sexist comments are made especially against people of a particular gender, whether the gender information of the users could be effective or not for sexism detection, is still an important question. Here we try to address this issue using Natural Language Processing (NLP) and machine learning models. Experiments showed that combining user gender information with textual features improved classification performance both in terms of binary classification and multi class classification. The effectiveness of the proposed strategy is demonstrated by the experimental results reported in this research. © 2023 IEEE.

关键词： Classification (of information)

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Fuzzy performance estimation of real-world driver’s stress recognition models based on physiological signals and deep learning approach

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Journal of Ambient Intelligence and Humanized Computing 2024年 1-16页

作者： Amin, Muhammad Ullah, Khalil Asif, Muhammad Shah, Habib Waheed, Abdul Din, Irfanud Department of Electronics University of Peshawar Peshawar25120 Pakistan Department of Physical & amp Numerical Sciences Qurtuba University of Science & ampIT Peshawar25100 Pakistan Department of Software Engineering University of Malakand Malakand23050 Pakistan Department of Computer Science King Khalid University Abha62529 Saudi Arabia Department of Computer Science Women University Swabi Swabi23430 Pakistan Department of Computer Science The Superior University Lahore Lahore54000 Pakistan

Driver’s mental stress is known as a prime factor in road crashes. The devastation of these crashes often results in losses of humans, vehicles, and infrastructure. Likewise, persistent mental stress could develop mental, cardiovascular, and abdominal disorders. Preceding research in this domain mostly focuses on feature engineering and conventional machine learning (ML) approaches. These approaches recognize different stress levels based on handcrafted features extracted from various modalities including physiological, physical, and contextual data. Acquiring the good quality features from these modalities using feature engineering is often a difficult job. The recent developments in the form of deep learning (DL) algorithms have relieved feature engineering by automatically extracting and learning resilient features. Conventional DL models, however, frequently over-fit due to large number of parameters. Thus, large networks face gradient vanishing issues causing an increase in learning failure and generalization errors. Furthermore, it is often hard to acquire a large dataset for training a deep learning model from scratch. To overcome these problems for driver’s stress recognition domain, this paper proposes fast and computationally efficient deep transfer learning models based on Xception pre-trained neural networks. These models classify the driver’s Low, Medium, and High stress levels through electrocardiogram (ECG), heart rate (HR), galvanic skin response (GSR), electromyogram (EMG), and respiration (RESP) signals. Continuous Wavelet Transform (CWT) acquires the scalograms for ECG, HR, GSR, EMG, and RESP signals separately. Then unimodal Xception models are trained based on these scalograms to classify the three stress levels. The proposed Xception models have achieved 97.2%, 86.4%, 82.7%, 71.9%, and 68.9% average validation accuracies based on ECG, RESP, HR, GSR, and EMG signals, respectively. The fuzzy EDAS (evaluation based on distance from average solutio

关键词： Electrocardiograms

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Residential Electricity Power Forecasting using GRU-based CNN and SAE Ensembler

Residential Electricity Power Forecasting using GRU-based CN...

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2023 IEEE International Conference on Emerging Trends in engineering, Sciences and Technology, ICES and T 2023

作者： Raza, Ali Ali, Usman Ali, Zain Saeed, Muhammad Hamza Khan, Raja Jalees Ul Hussen Ayub, Nasir COMSATS University Islamabad Department of Computer Science WahCantt Campus Pakistan Riphah International University Computing Department Faisalabad Pakistan HITEC University Department of Electrical Engineering Taxila Pakistan National Textile University Department of Computer Science Faisalabad Pakistan Superior University Gold Campus Department of Software Engineering Lahore Pakistan Capital University of Science and Technology Department of Software Engineering Islamabad Pakistan

ISBN: (纸本)9781665455602

Smart grids play an important role to resolves issues related to electricity. Electrical load forecasting can be performed through smart grids to acquire knowledge about the electrical load that will be needed in the future. A combination of Stacked Autoencoding (SAE) and Gated Recurrent Unit (GRU) and a combination of Convolutional Neural Network (CNN), Grey Wolf Optimization (GWO), and GRU are proposed in this paper. Firstly, the historical load data is preprocessed and then preprocessed data is given to the feature selection and extraction methods to measure the importance of each feature. Features that have highest values are selected and input to the proposed classifiers. According to the results the proposed CNN-GRU-GWO gave the highest accuracy rate of 96.31%. This application of this article will help the utility purchase the exact amount of electricity from the grid, which will lead to grid stability. © 2023 IEEE.

关键词： Smart power grids

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Batch Execution of Microbenchmarks for Efficient Performance Testing

Batch Execution of Microbenchmarks for Efficient Performance...

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International Conference on software Testing, Verification, and Validation, ICST

作者： Mostafa Jangali Kundi Yao Yiming Tang Diego Elias Costa Weiyi Shang Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada Department of Software Engineering Rochester Institute of Technology USA Computer Science and Software Engineering Concordia University Montreal Canada

ISBN: (数字)9798331508142

ISBN: (纸本)9798331508159

Performance microbenchmarking is essential for ensuring software quality by providing granular insights into code efficiency. While automated performance microbenchmark generation tools (e.g., ju2jmh) are proposed to alleviate practitioners from manually curating microbenchmarks, the high volume of generated benchmarks can lead to protracted benchmarking execution time, as many of the generated benchmarks are too short in nature to be valuable for evaluating performance. In this paper, we present a novel approach that optimizes microbenchmark execution through a batching strategy, i.e., grouping benchmarks with similar code coverage and treating them as a single unit to 1) reduce execution overhead and 2) reduce the bias from microbenchmarks that are too short. We evaluate the effectiveness of this enhancement across various Java projects, comparing the execution times of clustered and individual micro benchmarks. Our findings demonstrate substantial improvements in execution efficiency, reducing execution time by up to 89.81% while preserving high microbenchmark stability.

关键词： software testing Java Codes software quality software performance Benchmark testing

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The Spatial Spillover Effect of College Students’ Entrepreneurial Activeness Degree

International Journal of Emerging Technologies in Learning

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International Journal of Emerging Technologies in Learning 2022年第16期17卷 194-208页

作者： Chen, Huanhuan Wang, Chunbao Pang, Jin Computer Department Hebei University of Water Resources and Electric Engineering Cangzhou China School of Public Education Department Hebei Software Institute Baoding China

Analyzing the influence of the spatial spillover effect of college students’ Entrepreneurial Activeness Degree (EAD) in each region on the cultivation intensity of their Innovation and Entrepreneurial Ability (IEA) is of certain theoretical and practical significance. However, in the research of domestic scholars, when studying the relationship between the EAD of college students and the cultivation intensity of their IEA, they generally give comprehensive evaluations based on the Evaluation Index Systems (EISs) they built, few of them have concerned about the spatial spillover effect of the EAD of college students, and relevant analysis based on spatial measurement method is rarely seen. To fill in this research blank, this paper aims to study the spatial spillover effect of the EAD of college students based on the improvement of IEA. At first, the paper gave the analysis framework of EAD, and adopted a layer-by-layer vertical and horizontal scatter degree method to perform dynamic measurement on the EAD of college students based on evaluation data collected from different regions. Then, this paper built a college student innovation and entrepreneurial Knowledge Production Function (KPF) model and used it to analyze the relationship between IEA cultivation intensity and the EAD of college students, thereby attaining the development status and spatial spillovers of the entrepreneurial activities of college students. At last, experimental results proved the effectiveness of the measurement method and the constructed model, and the analysis results of the spatial spillover effect in the study regions were given. © 2022, International Journal of Emerging Technologies in Learning. All Rights Reserved.

关键词： Students

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Transforming Probabilistic Timed Automata to PRISM Model 26

Transforming Probabilistic Timed Automata to PRISM Model

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26th International computer Science and engineering Conference, ICSEC 2022

作者： Sutalungka, Thirawat Chariyathitipong, Patimakorn Vatanawood, Wiwat Chulalongkorn University Department of Computer Engineering Bangkok Thailand Sisaket Rajabhat University Department of Software Engineering Sisaket Thailand

ISBN: (纸本)9781665491983

The probabilistic timed automata are the models used to represent the probabilistic and time characteristics of systems to analyze the behavior of the system, including communication and multimedia. PRISM is a probabilistic model checker, a tool for formal modeling and probabilistic behavior analysis of systems. The PRISM language is used to construct the probabilistic timed automata to analyze and verify systems with PRISM. This paper proposes a set of probabilistic timed automata transform rules for transforming XML elements that represent probabilistic timed automata to PRISM model. The result can be used to analyze probabilistically and verify complex properties by the PRISM Model Checker. © 2022 IEEE.

关键词： Automata theory

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Algorithm of Coordination of Swarm of Autonomous Robots

Algorithm of Coordination of Swarm of Autonomous Robots

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2023 IEEE International Conference on Smart Information Systems and Technologies, SIST 2023

作者： Kyzyrkanov, Abzal Atanov, Sabyrzhan Aljawarneh, Shadi Tursynova, Nazira Kassymkhanov, Samat Astana It University L. N. Gumilyov Eurasian National University Department of Computer Engineering Astana Kazakhstan L. N. Gumilyov Eurasian National University Computer and Software Engineering Astana Kazakhstan Jordan University of Science and Technology Software Engineering Department Irbid Jordan L. N. Gumilyov Eurasian National University Department of Artificial Intelligence Technologies Astana Kazakhstan Astana It University Department of Computer Engineering Astana Kazakhstan

ISBN: (纸本)9798350335040

The task of keeping a specific geometric configuration while following a designated path is common in various fields that involve autonomous robots. When done correctly, this approach can result in several advantages, including a reduction in system costs and an increase in system reliability and efficiency, while maintaining the adaptability and flexibility of the system. The concept of maintaining a particular geometric pattern during motion is frequently utilized in different scenarios. For example, unmanned military vehicles must maintain a particular formation to explore and cover terrain during missions. Another example is in 'Smart Highways,' where the traffic system's capacity can be enhanced significantly by having vehicles move in groups at the same speed while maintaining a certain distance between them. This research presents an algorithm that allows a group of self-governing mobile robots to move while maintaining a specific geometric structure. A behavior-based control algorithm is advanced to regulate the movement of these systems. The coordination of the robots within the system is accomplished through the implementation of a leader-follower technique, with virtual leader. © 2023 IEEE.

关键词： Military vehicles

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An IoT-Based Smart Home for Elderly Suffering from Dementia 1

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8th International Conference on Smart City Applications, SCA 2023

作者： Wasim Raed, Mhd. Huseyinov, Ilham Ozdemir, Ghina Kotenko, Igor Fedorchenko, Elena Department of Computer Engineering Istanbul Aydin University Istanbul Turkey Software Engineering Department Istanbul Aydin University Istanbul Turkey St. Petersburg Federal Research Center of the Russian Academy of Sciences St. Petersburg Russia

ISBN: (数字)9783031538247

ISBN: (纸本)9783031538230

There exists a global challenge related to the boosting number of elderly suffering from chronic diseases like Dementia (EWD). Hence, there is a drastic need for cost-effective disruptive technologies that enable and guarantee the quality of life for EWD via implementing telehealth. The purpose of this paper is to propose a telehealth system based on the Internet of Things for the well-being of the elderly suffering from Dementia in a smart home setting. We propose a smart home solution as a whole and a solution based on an NFC-based working prototype of a pill intake in a box for monitoring and recording the proper intake of prescribed medication for the elderly suffering from chronic diseases. A smart home solution was effectively implemented using the Cisco Packet Tracer simulator 7.3. The proposed IoT-based solution ensures the safe wandering of the elderly in a smart home setting using a Cisco packet tracer simulation tool. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Cost effectiveness

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