## 2 Literature Review
### Research on Artificial Intelligence Technology in the Field of Software Engineering  
AI-enabled software engineering (AISE) has entered the peak of practical application [4]. The application of AI in software engineering is primarily reflected in three core dimensions: requirements engineering, development paradigms, and quality assurance systems. To be specific, it is manifested as follows:  
(1) Semantic reconstruction in requirements engineering. Requirements engineering focuses on constructing functional and non-functional specifications for target systems [5; 6]. Early research predominantly centered on model-based classification, for example, [PERSON] et al. [7] conducted semantic classification of requirement texts based on the Bi-LSTM model, and [PERSON] et al. [8] utilized the pre-trained language BERT model [9] to automatically extract requirement features from user comments. With the development of large language models (LLMs), exemplified by GPT [10] and Qwen-VL [11], the demand engineering approach enters a new paradigm: Automated extraction and formalization of requirement elements through semantic correlation analysis, context-aware dynamic generation of requirement documentation, and knowledge graph-enhanced dependency modeling and conflict detection [12; 13; 14].  
(2) Cognitive reconfiguration and knowledge transfer in development paradigms: The traditional development model uses a linear process to promote the progress of each development stage, which exposes defects such as response delay and experience dependence when facing complex system development [15]. Prior improvements focused narrowly on automating isolated development tasks, [PERSON] et al. [16] proposed the detection of code anomalies based on machine learning methods, and [PERSON] et al. [17] used Sentent-BERT's recommended code API. The advent of LLMs has fundamentally redefined the technical ecosystem of intelligent development, demonstrating robust capabilities in code generation and autocompletion (e.g., GitHub Copilot [18]), defect pattern recognition (e.g., DeepDebug [19]), and semantic code comprehension [20]. The use of LLMs effectively solves the fragmentation problem of the traditional tool chain, and realizes the systematic precipitation and reuse of development experience.  
(3) Intelligent evolution of quality assurance systems. Conventional approaches, constrained by manually designed test cases and scripted regression testing, struggle with inadequate coverage and exponentially rising maintenance costs [21; 22]. Current optimizations target isolated techniques, for example, [PERSON] et al. [23] applied a genetic algorithm to automatically generate test datasets, and [PERSON] et al. [24] generated test cases based on diffusion model. LLMs propel quality assurance into a cognitive intelligence era: [PERSON] et al. [25] use LLMs to implement defect pattern detection and suggestions; reinforcement learning [26] integrated frameworks optimize test data generation through code path coverage reward functions, simultaneously enhancing coverage [27]. This intelliggent evolution establishes a novel technical paradigm for quality assurance in continuous delivery scenarios.  
The current software development landscape is plagued by fragmented technology integration and isolated toolchains, impeding the transformation of localized task optimization into overall efficiency gains. AI-human interaction remains fragmented both vertically and horizontally, with a lack of dynamic coupling across roles and a deficiency in mutual interpretability and trust between human expertise and AI logic. Additionally, behavioral data from development processes are siloed and underutilized, lacking cross-project knowledge fusion and robust decision support, especially in large-scale distributed systems.