A REVIEW OF ENHANCE CODE QUALITY AND DEVELOPMENT EFFICIENCY BY BIG DATA INFRASTRUCTURE

Aarati Chavan; Uruj Jaleel; Nisha Auti

doi:10.29121/shodhkosh.v5.i6.2024.2495

Authors

Aarati Chavan Research Scholar, Department of Computer Science and Engineering, Kalinga University, Raipur 492010
Dr. Uruj Jaleel Department of Computer Science and Engineering, Kalinga University, Raipur 492010
Dr. Nisha Auti Department of Computer Science and Engineering, Bharati Vidyapith, Pune 411030

DOI:

https://doi.org/10.29121/shodhkosh.v5.i6.2024.2495

Keywords:

Recommendations, Software, Big Data, Techniques, Mobile

Abstract [English]

In the rapidly evolving landscape of software development, the need for high code quality and efficient development processes is paramount. The integration of Big Data infrastructure into software development workflows has emerged as a powerful approach to enhancing both code quality and development efficiency. This review explores how Big Data technologies can be leveraged to analyze vast amounts of code, identify patterns, predict potential bugs, and optimize development practices. This review underscores the transformative potential of Big Data in revolutionizing software development. The use of recommendation systems while developing software increasing in order to speed up the process of software development by software developers. Accurate recommendations leads to successful, faster, efficient development, but inaccurate recommendations can lead to inappropriate, missed deadline software development.

References

Ricardo Tubio ; Rafael Sotelo ; Yolanda Blanco ; Martin Lopez ; Alberto Gil ; Jose Pazos ; Manuel Ramos, “A TV-anytime metadata approach to TV program recommendation for groups”, 2008 IEEE International Symposium on Consumer Electronics, 2008. DOI: https://doi.org/10.1109/ISCE.2008.4559529

Janez Zaletelj; Richard Wages; Tobias Burger; Stefan M. Grunvogel, “Content Recommendation System in the Production of Multi-Channel TV Programs”, Third International Conference on Automated Production of Cross Media Content for Multi-Channel Distribution (AXMEDIS'07), 2007. DOI: https://doi.org/10.1109/AXMEDIS.2007.30

Yu Yan; Kohei Hara; Takenobu Kazuma; Aiguo He, “A Method for Personalized C Programming Learning Contents Recommendation to Enhance Traditional Instruction”, 2017 IEEE 31st International Conference on Advanced Information Networking and Applications (AINA), 2017. DOI: https://doi.org/10.1109/AINA.2017.13

Carlos Becerra ; Roberto Muñoz ; René Noël ; Marta Barría, “Learning objects recommendation platform based on learning styles for programming fundamentals”,2016 XI Latin American Conference on Learning Objects and Technology (LACLO), 2016. DOI: https://doi.org/10.1109/LACLO.2016.7751771

Fatmah Yousef Assiri, “Recommendations to improve programming skills of students of computer science”, 2016 SAI Computing Conference (SAI), 2016.

Mengyi Zhang ; Minyong Shi ; Zhiguo Hong ; Songtao Shang ; Menghan Yan, “A TV program recommendation system based on big data”, 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS), 2016. DOI: https://doi.org/10.1109/ICIS.2016.7550923

Davoudian, Ali, and Mengchi Liu. "Big data systems: A software engineering perspective." ACM Computing Surveys (CSUR) 53.5 (2020): 1-39. DOI: https://doi.org/10.1145/3408314

Hu, Han, et al. "Toward scalable systems for big data analytics: A technology tutorial." IEEE access 2 (2014): 652-687. DOI: https://doi.org/10.1109/ACCESS.2014.2332453

Elshawi, Radwa, et al. "Big data systems meet machine learning challenges: towards big data science as a service." Big data research 14 (2018): 1-11. DOI: https://doi.org/10.1016/j.bdr.2018.04.004

Mavrogiorgos, Konstantinos, et al. "Self-Adaptable Infrastructure Management for Analyzing the Efficiency of Big Data Stores." Journal of Advances in Information Technology Vol 13.5 (2022). DOI: https://doi.org/10.12720/jait.13.5.423-432

Oussous, Ahmed, et al. "Big Data technologies: A survey." Journal of King Saud University-Computer and Information Sciences 30.4 (2018): 431-448. DOI: https://doi.org/10.1016/j.jksuci.2017.06.001

Khan, Nawsher, et al. "Big data: survey, technologies, opportunities, and challenges." The scientific world journal 2014.1 (2014): 712826. DOI: https://doi.org/10.1155/2014/712826

Jiangshan Xu ; Liang-Jie Zhang ; Haiming Lu ; Yanda Li, “The development and prospect of personalized TV program recommendation systems”, Fourth International Symposium on Multimedia Software Engineering, 2002. Proceedings, 2002.

Jinshui Wang ; Xin Peng ; Zhenchang Xing ; Kun Fu ; Wenyun Zhao, “Contextual Recommendation of Relevant Program Elements in an Interactive Feature Location Process”,2017 IEEE 17th International Working Conference on Source Code Analysis and Manipulation (SCAM), 2017. DOI: https://doi.org/10.1109/SCAM.2017.14

Fulian Yin ; Xiaowei Liu ; Wanying Ding ; Ruizhe Zhang, “Tag-Based collaborative filtering recommendation algorithm for TV program”, 2016 13th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), 2016.