Software engineering integrates principles of computer science to design, develop, and maintain software applications. As technology advances, the complexity of software systems increases, leading to challenges in ensuring efficiency, accuracy, and overall performance. artificial intelligence, particularly the use of large language models (LLMs), has had a significant impact on this field. LLMs now automate tasks such as code generation, debugging, and software testing, reducing human involvement in these repetitive tasks. These approaches are becoming critical in addressing the growing challenges of modern software development.
One of the main challenges in software engineering is managing the increasing complexity of software systems. As software scales, traditional methods often fail to meet the demands of modern applications. Developers need help generating reliable code, detecting vulnerabilities, and ensuring functionality throughout development. This complexity requires solutions that assist with code generation and seamlessly integrate multiple tasks, minimizing errors and improving overall development speed.
Current tools used in software engineering, such as LLM-based models, help developers by automating tasks such as code summarization, error detection, and code translation. However, while these tools provide automation, they are typically designed for specific task-specific functions. They often need a cohesive framework to integrate the full spectrum of software development tasks. This fragmentation limits their ability to address the broader context of software engineering challenges, leaving room for further innovation.
Researchers from Sun Yat-sen University, Xi'an Jiaotong University, Shenzhen Institute of Advanced technology, Xiamen University, and Huawei Cloud Computing Technologies have proposed a new framework to address these challenges. This framework uses LLM-driven agents for software engineering tasks and includes three key modules: perception, memory, and action. The perception module processes various inputs such as text, images, and audio, while the memory module organizes and stores this information for future decision making. The action module uses this information to make informed decisions and perform tasks such as code generation, debugging, and other software development activities.
The framework’s methodology involves these modules working together to automate complex workflows. The perception module processes inputs and converts them into a format that LLMs can understand. The memory module stores different types of information, such as semantic, episodic, and procedural memory, which are used to improve decision making. The action module combines inputs and memory to execute tasks such as code generation and debugging, learning from previous actions to improve future outcomes. This integrated approach improves the system’s ability to handle various software engineering tasks with greater contextual awareness.
The study highlighted several performance challenges in implementing this framework. A major issue identified was hallucinations produced by LLM-based agents, such as the generation of non-existent APIs. These hallucinations affect the reliability of the system, and mitigating them is critical to improving performance. The framework also faces challenges in multi-agent collaboration, where agents must synchronize and share information, leading to higher computational costs and communication overheads. The researchers noted that improving resource efficiency and reducing these communication costs is essential to improving overall system performance.
The study also discusses areas for future research, in particular the need to address hallucinations generated by LLMs and to optimize collaboration processes between multiple agents. These critical challenges need to be resolved to fully realize the potential of LLM-based agents in software engineering. Furthermore, incorporating more advanced software engineering technologies into these frameworks could enhance their capabilities, especially in handling complex software projects.
In conclusion, the research offers a comprehensive framework to address the growing challenges in software engineering by leveraging LLM-based agents. The proposed system integrates perception, memory, and action modules to automate key tasks such as code generation, debugging, and decision making. While the framework demonstrates potential, the study emphasizes opportunities for improvement, particularly in reducing hallucinations and improving efficiency in multi-agent collaboration. The contributions from Sun Yat-sen University and Huawei Cloud Computing mark a significant advancement in integrating ai technologies into practical software engineering applications.
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Nikhil is a Consultant Intern at Marktechpost. He is pursuing an integrated dual degree in Materials from Indian Institute of technology, Kharagpur. Nikhil is an ai and Machine Learning enthusiast who is always researching applications in fields like Biomaterials and Biomedical Science. With a strong background in Materials Science, he is exploring new advancements and creating opportunities to contribute.
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