ArticleScientific reports2025
SynergyBug: A deep learning approach to autonomous debugging and code remediation.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Bug detection and resolution are pivotal to maintaining the quality, reliability, and performance of software systems. Manual debugging, along with traditional static rule-based methods, proves inefficient when applied to complex software structures in contemporary times. SynergyBug combines BERT and GPT-3 to autonomously detect and repair bugs across multiple sources. It resolves essential requirements by implementing an automated system that diagnoses and resolves software bugs automatically, thus minimising human involvement. The framework unites BERT as a contextual machinery with GPT-3 to produce bug fix generation capabilities. The semantic pattern within bug reports, together with error logs and documentation, feeds into BERT for contextual embedding generation. GPT-3 applies the generated embeddings to produce code fixes, code snippets, as well as detailed explanations that address detected problems. The system achieves continuous automatic debugging by enhancing both detection and resolution steps into one unified process. The experimental outcomes prove that it achieves superior performance than conventional bug detection methods by reaching 98.79% accuracy alongside 97.23% precision and 96.56% recall. The system demonstrated exceptional detection strength for functional and performance, and security bugs, where the detection rates reached 94% and 90% and 92%, respectively. SynergyBug showed its ability to expand as it processed bug reports exceeding 100,000 cases without noticeably impacting system performance. This proposed system provides faster debugging capabilities to improve the quality of the complete software development process. This paper discusses as a tool that can revolutionise bug management through proactive instead of just reactive strategies. The implementation of human monitoring within safety programs and managing training system biases represent essential organisational factors. The study terminates by recognising SynergyBug as a crucial development leading toward automated debugging tools that maintain operational safety within intricate software systems.
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