Interactive Debugging through Natural Language Inputs: Bridging the Gap between Human and Machine Understanding
DOI:
https://doi.org/10.32628/CSEIT25111234Keywords:
Natural Language Debugging, Interactive Programming Environments, Machine Learning Applications, Software Development Automation, Human-Computer InteractionAbstract
Interactive debugging through natural language inputs represents a transformative approach in software development, bridging the gap between human communication and machine understanding. This innovation addresses the challenges faced by novice programmers and non-technical stakeholders in traditional debugging environments. By integrating sophisticated Natural Language Processing models with debugging tools, the system enables users to describe programming issues in their native language while receiving contextually relevant solutions. The architecture encompasses input processing, context analysis, suggestion generation, and interactive feedback mechanisms, leading to enhanced debugging efficiency and improved developer productivity. Through empirical evaluations and user experience assessments, the implementation demonstrates significant improvements in query interpretation accuracy, response generation time, and overall debugging effectiveness. The system's impact extends beyond technical improvements, fostering better collaboration between technical and non-technical team members while making software development more accessible to diverse user groups.
📊 Article Downloads
References
S. Zhang, Y. Lin, M. D. Ernst, "Automated Diagnosis of Software Configuration Errors," in Proceedings of the 35th International Conference on Software Engineering, San Francisco, CA, USA, 2013, Available: https://homes.cs.washington.edu/~mernst/pubs/configuration-errors-icse2013.pdf DOI: https://doi.org/10.1109/ICSE.2013.6606577
J. Good, K. Howland, K. Nicholson, "Programming Language, Natural Language? Supporting the Diverse Computational Activities of Novice Programmers," 2016. Available: https://www.researchgate.net/publication/309545412_Programming_Language_Natural_Language_Supporting_the_Diverse_Computational_Activities_of_Novice_Programmers DOI: https://doi.org/10.1016/j.jvlc.2016.10.008
P. Dogga, et al., "A System-Wide Debugging Assistant Powered by Natural Language Processing," 2019, Available: https://www.cs.princeton.edu/~ravian/publications/nlpdebugging_socc19.pdf DOI: https://doi.org/10.1145/3357223.3362701
A. Dutilleul, X. Qiu, M. Martonosi, "Performance Debugging through Microarchitectural Sensitivity and Causality Analysis," 2024. Available: https://arxiv.org/html/2412.13207v1
D. Bunting, "How to use GenAI for database query optimization and natural language analysis," 2024. Available: https://www.chaossearch.io/blog/genai-database-query-natural-language
D. Nadeau et al., "Efficient large-scale heterogeneous debugging using dynamic tracing," 2019. Available: https://www.sciencedirect.com/science/article/abs/pii/S1383762118301838 DOI: https://doi.org/10.1016/j.sysarc.2019.02.016
A. Felfernig, G. Friedrich, M. Jannach, "Debugging user interface descriptions of knowledge-based recommender applications," 2006, Available: https://www.researchgate.net/publication/221607628_Debugging_user_interface_descriptions_of_knowledge-based_recommender_applications DOI: https://doi.org/10.1145/1111449.1111499
N. Scharowski, F. Guede, L. Schmidt, "Exploring the effects of human-centered AI explanations on trust and reliance," 2023. Available: https://www.researchgate.net/publication/372434717_Exploring_the_effects_of_human-centered_AI_explanations_on_trust_and_reliance DOI: https://doi.org/10.3389/fcomp.2023.1151150
D. R. Woloschuk et al., "Performance debugging in the enterprise parallel programming system," 1995, Available: https://www.researchgate.net/publication/221500738_Performance_debugging_in_the_enterprise_parallel_programming_system
S. C. Necula et al., "A Systematic Literature Review on Using Natural Language Processing in Software Requirements Engineering," 2024. Available: https://www.researchgate.net/publication/380865266_A_Systematic_Literature_Review_on_Using_Natural_Language_Processing_in_Software_Requirements_Engineering DOI: https://doi.org/10.3390/electronics13112055
J. Davis, "AI-Powered Tools for Debugging and Testing in Software Development – A Complete Guide," 2024. Available: https://www.linkedin.com/pulse/ai-powered-tools-debugging-testing-software-development-complete-rtsgf
M. Roberts, S. Wilson, "Natural language processing (NLP) in software development," 2024. Available: https://github.com/resources/articles/ai/natural-language-processing
Downloads
Published
Issue
Section
License
Copyright (c) 2025 International Journal of Scientific Research in Computer Science, Engineering and Information Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
