Software Product Line Engineering - Advances and Practices: Investigating advances and practices in software product line engineering (SPLE) for developing families of related software products
Keywords:
Software Product Line Engineering, SPLE, Product Variability, Feature Modeling, Domain Engineering, Application Engineering, Reuse, Variability Management, Tool Support, Case StudiesAbstract
Software Product Line Engineering (SPLE) has emerged as a promising approach for developing families of related software products efficiently. This paper provides an overview of the recent advances and best practices in SPLE, focusing on its key concepts, methodologies, and tools. It discusses the benefits and challenges of SPLE adoption and highlights successful case studies. The paper also explores future research directions in SPLE to address evolving software engineering needs.
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References
Alghayadh, Faisal Yousef, et al. "Ubiquitous learning models for 5G communication network utility maximization through utility-based service function chain deployment." Computers in Human Behavior (2024): 108227.
Pargaonkar, Shravan. "A Review of Software Quality Models: A Comprehensive Analysis." Journal of Science & Technology 1.1 (2020): 40-53.
MURAVEV, M., et al. "HYBRID SOFTWARE DEVELOPMENT METHODS: EVOLUTION AND THE CHALLENGE OF INFORMATION SYSTEMS AUDITING." Journal of the Balkan Tribological Association 29.4 (2023).
Pulimamidi, Rahul. "Emerging Technological Trends for Enhancing Healthcare Access in Remote Areas." Journal of Science & Technology 2.4 (2021): 53-62.
Raparthi, Mohan, Sarath Babu Dodda, and Srihari Maruthi. "AI-Enhanced Imaging Analytics for Precision Diagnostics in Cardiovascular Health." European Economic Letters (EEL) 11.1 (2021).
Kulkarni, Chaitanya, et al. "Hybrid disease prediction approach leveraging digital twin and metaverse technologies for health consumer." BMC Medical Informatics and Decision Making 24.1 (2024): 92.
Raparthi, Mohan, Sarath Babu Dodda, and SriHari Maruthi. "Examining the use of Artificial Intelligence to Enhance Security Measures in Computer Hardware, including the Detection of Hardware-based Vulnerabilities and Attacks." European Economic Letters (EEL) 10.1 (2020).
Dutta, Ashit Kumar, et al. "Deep learning-based multi-head self-attention model for human epilepsy identification from EEG signal for biomedical traits." Multimedia Tools and Applications (2024): 1-23.
Raparthy, Mohan, and Babu Dodda. "Predictive Maintenance in IoT Devices Using Time Series Analysis and Deep Learning." Dandao Xuebao/Journal of Ballistics 35: 01-10.
Kumar, Mungara Kiran, et al. "Approach Advancing Stock Market Forecasting with Joint RMSE Loss LSTM-CNN Model." Fluctuation and Noise Letters (2023).
Raparthi, Mohan. "Biomedical Text Mining for Drug Discovery Using Natural Language Processing and Deep Learning." Dandao Xuebao/Journal of Ballistics 35
Sati, Madan Mohan, et al. "Two-Area Power System with Automatic Generation Control Utilizing PID Control, FOPID, Particle Swarm Optimization, and Genetic Algorithms." 2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT). IEEE, 2024.
Raparthy, Mohan, and Babu Dodda. "Predictive Maintenance in IoT Devices Using Time Series Analysis and Deep Learning." Dandao Xuebao/Journal of Ballistics 35: 01-10.
Pulimamidi, Rahul. "Leveraging IoT Devices for Improved Healthcare Accessibility in Remote Areas: An Exploration of Emerging Trends." Internet of Things and Edge Computing Journal 2.1 (2022): 20-30.
Reddy, Byrapu, and Surendranadha Reddy. "Evaluating The Data Analytics For Finance And Insurance Sectors For Industry 4.0." Tuijin Jishu/Journal of Propulsion Technology 44.4 (2023): 3871-3877.
Reddy, Surendranadha Reddy Byrapu. "Big Data Analytics-Unleashing Insights through Advanced AI Techniques." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 1-10.
