Neural Program Synthesis using Multimodal Reasoning for Automated Software Generation
Main Article Content
Abstract
The rapid evolution of artificial intelligence, fueled by advances in deep learning and generative modeling, has catalyzed a paradigm shift in automated software development. Traditional program synthesis approaches rely heavily on symbolic reasoning, rule-based logic, or domain-specific languages, often limiting their scalability, adaptability, and expressiveness. In contrast, the emergence of neural networks—particularly transformer-based large language models (LLMs)—has demonstrated remarkable capabilities in generating syntactically correct and semantically meaningful code. However, these models still face limitations when handling complex programming tasks that require richer contextual understanding, grounding in real-world semantics, or integration across heterogeneous data modalities. To address these challenges, this research proposes a novel framework titled Neural Program Synthesis Using Multimodal Reasoning (NPS-MMR), which leverages multimodal inputs—including natural language descriptions, code snippets, diagrams, execution traces, and GUI screenshots—to produce more accurate, context-aware software automatically.
The proposed framework integrates three key components: (1) Multimodal Knowledge Encoder, (2) Reasoning and Alignment Engine, and (3) Generative Program Synthesizer. The Multimodal Encoder transforms diverse data formats into a unified semantic space using cross-attention and contrastive alignment mechanisms. This enables the system to learn correspondences between textual requirements, visual representations like flowcharts or UI wireframes, and programmatic structures. The Reasoning and Alignment Engine enhances interpretability and performance through neuro-symbolic reasoning, constraint satisfaction, and error propagation analysis. It ensures that generated code adheres to both functional requirements and design specifications. Finally, the Generative Program Synthesizer—built on top of a customized transformer-based decoder—uses chain-of-thought and execution-guided decoding to generate source code that is syntactically valid, logically coherent, and optimized for runtime performance.
Article Details
Section
How to Cite
References
1. Kodela, V. INTELLIGENT SYSTEMS AND APPLICATIONS IN ENGINEERING.
2. Kodela, V. (2016). Improving load balancing mechanisms of software defined networks using open flow. California State University, Long Beach.
3. Kodela, V. (2018). A Comparative Study Of Zero Trust Security Implementations Across Multi-Cloud Environments: Aws And Azure. Int. J. Commun. Networks Inf. Secur.
4. Nandhan, T. N. G., Sajjan, M., Keshamma, E., Raghuramulu, Y., & Naidu, R. (2005). Evaluation of Chinese made moisture meters.
5. Gupta, P. K., Mishra, S. S., Nawaz, M. H., Choudhary, S., Saxena, A., Roy, R., & Keshamma, E. (2020). Value Addition on Trend of Pneumonia Disease in India-The Current Update.
6. Hiremath, L., Sruti, O., Aishwarya, B. M., Kala, N. G., & Keshamma, E. (2021). Electrospun nanofibers: Characteristic agents and their applications. In Nanofibers-Synthesis, Properties and Applications. IntechOpen.
7. Manikandan, G., & Srinivasan, S. (2012). Traffic control by bluetooth enabled mobile phone. International Journal of Computer and Communication Engineering, 1(1), 66.
8. Manikandan, G., and G. Bhuvaneswari. "Fuzzy-GSO Algorithm for Mining of Irregularly Shaped Spatial Clusters." Asian Journal of Research in Social Sciences and Humanities 6, no. 6 (2016): 1431-1452.
9. Manikandan, G., & Srinivasan, S. A Novel Approach for effectively mining for spatially co-located moving objects from the spatial data base. International Journal on “CiiT International Journal of Data Mining and Knowledge Engineering, 816-821.
10. Nagar, H., & Menaria, A. K. Compositions of the Generalized Operator (????�????�, ????�, ????�, ????�; ????� ????�)(????�) and their Application.
11. Nagar, H., & Menaria, A. K. On Generalized Function Gρ, η, γ [a, z] And It’s Fractional Calculus.
12. Singh, R., & Menaria, A. K. (2014). Initial-Boundary Value Problems of Fokas’ Transform Method. Journal of Ramanujan Society of Mathematics and Mathematical Sciences, 3(01), 31-36.
13. Sumanth, K., Subramanya, S., Gupta, P. K., Chayapathy, V., Keshamma, E., Ahmed, F. K., & Murugan, K. (2022). Antifungal and mycotoxin inhibitory activity of micro/nanoemulsions. In Bio-Based Nanoemulsions for Agri-Food Applications (pp. 123-135). Elsevier.
14. Gupta, P. K., Lokur, A. V., Kallapur, S. S., Sheriff, R. S., Reddy, A. M., Chayapathy, V., ... & Keshamma, E. (2022). Machine Interaction-Based Computational Tools in Cancer Imaging. Human-Machine Interaction and IoT Applications for a Smarter World, 167-186.
15. Rajoriaa, N. V., & Menariab, A. K. (2022). Fractional Differential Conditions with the Variable-Request by Adams-Bashforth Moulton Technique. Turkish Journal of Computer and Mathematics Education Vol, 13(02), 361-367.
16. Khemraj, S., Thepa, P. C. A., Patnaik, S., Chi, H., & Wu, W. Y. (2022). Mindfulness meditation and life satisfaction effective on job performance. NeuroQuantology, 20(1), 830–841.
17. Sutthisanmethi, P., Wetprasit, S., & Thepa, P. C. A. (2022). The promotion of well-being for the elderly based on the 5 Āyussadhamma in the Dusit District, Bangkok, Thailand: A case study of Wat Sawaswareesimaram community. International Journal of Health Sciences, 6(3), 1391–1408.
18. Thepa, P. C. A. (2022). Buddhadhamma of peace. International Journal of Early Childhood, 14(3).
19. Phattongma, P. W., Trung, N. T., Phrasutthisanmethi, S. K., Thepa, P. C. A., & Chi, H. (2022). Phenomenology in education research: Leadership ideological. Webology, 19(2).
20. Khemraj, S., Thepa, P., Chi, A., Wu, W., & Samanta, S. (2022). Sustainable wellbeing quality of Buddhist meditation centre management during coronavirus outbreak (COVID-19) in Thailand using the quality function deployment (QFD), and KANO. Journal of Positive School Psychology, 6(4), 845–858.
21. Thepa, D. P. P. C. A., Sutthirat, N., & Nongluk (2022). Buddhist philosophical approach on the leadership ethics in management. Journal of Positive School Psychology, 6(2), 1289–1297.
22. Rajeshwari: Manasa R, K Karibasappa, Rajeshwari J, Autonomous Path Finder and Object Detection Using an Intelligent Edge Detection Approach, International Journal of Electrical and Electronics Engineering, Aug 2022, Scopus indexed, ISSN: 2348-8379, Volume 9 Issue 8, 1-7, August 2022. https://doi.org/10.14445/23488379/IJEEE-V9I8P101
23. Rajeshwari.J,K. Karibasappa ,M.T. Gopalkrishna, “Three Phase Security System for Vehicles using Face Recognition on Distributed Systems", Third International conference on informational system design and intelligent applications, Volume 3 , pp.563-571, 8-9 January, Springer India 2016. Index: Springer
24. Sunitha.S, Rajeshwari.J, Designing and Development of a New Consumption Model from Big Data to form Data-as-a- Product (DaaP), International Conference on Innovative Mechanisms for Industry Applications (ICIMIA 2017), 978- 1-5090-5960-7/17/$31.00 ©2017 IEEE.
25. M. Suresh Kumar, J. Rajeshwari & N. Rajasekhar," Exploration on Content-Based Image Retrieval Methods", International Conference on Pervasive Computing and Social Networking, ISBN 978-981-16-5640-8, Springer, Singapore Jan (2022).
26. Vadisetty, R., Polamarasetti, A., Guntupalli, R., Raghunath, V., Jyothi, V. K., & Kudithipudi, K. (2022). AI-Driven Cybersecurity: Enhancing Cloud Security with Machine Learning and AI Agents. Sateesh kumar and Raghunath, Vedaprada and Jyothi, Vinaya Kumar and Kudithipudi, Karthik, AI-Driven Cybersecurity: Enhancing Cloud Security with Machine Learning and AI Agents (February 07, 2022).
27. Polamarasetti, A., Vadisetty, R., Vangala, S. R., Chinta, P. C. R., Routhu, K., Velaga, V., ... & Boppana, S. B. (2022). Evaluating Machine Learning Models Efficiency with Performance Metrics for Customer Churn Forecast in Finance Markets. International Journal of AI, BigData, Computational and Management Studies, 3(1), 46-55.
28. Polamarasetti, A., Vadisetty, R., Vangala, S. R., Bodepudi, V., Maka, S. R., Sadaram, G., ... & Karaka, L. M. (2022). Enhancing Cybersecurity in Industrial Through AI-Based Traffic Monitoring IoT Networks and Classification. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(3), 73-81.
29. Vadisetty, R., Polamarasetti, A., Guntupalli, R., Rongali, S. K., Raghunath, V., Jyothi, V. K., & Kudithipudi, K. (2021). Legal and Ethical Considerations for Hosting GenAI on the Cloud. International Journal of AI, BigData, Computational and Management Studies, 2(2), 28-34.
30. Vadisetty, R., Polamarasetti, A., Guntupalli, R., Raghunath, V., Jyothi, V. K., & Kudithipudi, K. (2021). Privacy-Preserving Gen AI in Multi-Tenant Cloud Environments. Sateesh kumar and Raghunath, Vedaprada and Jyothi, Vinaya Kumar and Kudithipudi, Karthik, Privacy-Preserving Gen AI in Multi-Tenant Cloud Environments (January 20, 2021).
31. Vadisetty, R., Polamarasetti, A., Guntupalli, R., Rongali, S. K., Raghunath, V., Jyothi, V. K., & Kudithipudi, K. (2020). Generative AI for Cloud Infrastructure Automation. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 1(3), 15-20.
32. Gandhi Vaibhav, C., & Pandya, N. Feature Level Text Categorization For Opinion Mining. International Journal of Engineering Research & Technology (IJERT) Vol, 2, 2278-0181.
33. Gandhi, V. C., Prajapati, J. A., & Darji, P. A. (2012). Cloud computing with data warehousing. International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), 1(3), 72-74.
34. Gandhi, V. C. (2012). Review on Comparison between Text Classification Algorithms/Vaibhav C. Gandhi, Jignesh A. Prajapati. International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), 1(3).
35. Patel, D., Gandhi, V., & Patel, V. (2014). Image registration using log pola
36. Patel, D., & Gandhi, V. Image Registration Using Log Polar Transform.
37. Desai, H. M., & Gandhi, V. (2014). A survey: background subtraction techniques. International Journal of Scientific & Engineering Research, 5(12), 1365.
38. Maisuriya, C. S., & Gandhi, V. (2015). An Integrated Approach to Forecast the Future Requests of User by Weblog Mining. International Journal of Computer Applications, 121(5).
39. Maisuriya, C. S., & Gandhi, V. (2015). An Integrated Approach to Forecast the Future Requests of User by Weblog Mining. International Journal of Computer Applications, 121(5).
40. esai, H. M., Gandhi, V., & Desai, M. (2015). Real-time Moving Object Detection using SURF. IOSR Journal of Computer Engineering (IOSR-JCE), 2278-0661.
41. Gandhi Vaibhav, C., & Pandya, N. Feature Level Text Categorization For Opinion Mining. International Journal of Engineering Research & Technology (IJERT) Vol, 2, 2278-0181.
42. Singh, A. K., Gandhi, V. C., Subramanyam, M. M., Kumar, S., Aggarwal, S., & Tiwari, S. (2021, April). A Vigorous Chaotic Function Based Image Authentication Structure. In Journal of Physics: Conference Series (Vol. 1854, No. 1, p. 012039). IOP Publishing.
43. Gandhi, V. C., & Gandhi, P. P. (2022, April). A survey-insights of ML and DL in health domain. In 2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) (pp. 239-246). IEEE.
44. Dhinakaran, M., Priya, P. K., Alanya-Beltran, J., Gandhi, V., Jaiswal, S., & Singh, D. P. (2022, December). An Innovative Internet of Things (IoT) Computing-Based Health Monitoring System with the Aid of Machine Learning Approach. In 2022 5th International Conference on Contemporary Computing and Informatics (IC3I) (pp. 292-297). IEEE.
45. Dhinakaran, M., Priya, P. K., Alanya-Beltran, J., Gandhi, V., Jaiswal, S., & Singh, D. P. (2022, December). An Innovative Internet of Things (IoT) Computing-Based Health Monitoring System with the Aid of Machine Learning Approach. In 2022 5th International Conference on Contemporary Computing and Informatics (IC3I) (pp. 292-297). IEEE.
46. Sharma, S., Sanyal, S. K., Sushmita, K., Chauhan, M., Sharma, A., Anirudhan, G., ... & Kateriya, S. (2021). Modulation of phototropin signalosome with artificial illumination holds great potential in the development of climate-smart crops. Current Genomics, 22(3), 181-213.
47. Patchamatla, P. S. (2022). Performance Optimization Techniques for Docker-based Workloads.
48. Patchamatla, P. S. (2020). Comparison of virtualization models in OpenStack. International Journal of Multidisciplinary Research in Science, Engineering and Technology, 3(03).
49. Patchamatla, P. S., & Owolabi, I. O. (2020). Integrating serverless computing and kubernetes in OpenStack for dynamic AI workflow optimization. International Journal of Multidisciplinary Research in Science, Engineering and Technology, 1, 12.
50. Patchamatla, P. S. S. (2019). Comparison of Docker Containers and Virtual Machines in Cloud Environments. Available at SSRN 5180111.
51. Patchamatla, P. S. S. (2021). Implementing Scalable CI/CD Pipelines for Machine Learning on Kubernetes. International Journal of Multidisciplinary and Scientific Emerging Research, 9(03), 10-15662.
52. Khemraj, S., Chi, H., Wu, W. Y., & Thepa, P. C. A. (2022). Foreign investment strategies. Performance and Risk Management in Emerging Economy, resmilitaris, 12(6), 2611–2622.
53. Anuj Arora, “Analyzing Best Practices and Strategies for Encrypting Data at Rest (Stored) and Data in Transit (Transmitted) in Cloud Environments”, International Journal of Research in Electronics and Computer Engineering, Vol. 6, Issue 4 (October–December 2018).