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AI Empowered Program Performance Analysis.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
AI Empowered Program Performance Analysis./
作者:
Liang, Yuchen.
面頁冊數:
1 online resource (27 pages)
附註:
Source: Masters Abstracts International, Volume: 85-01.
Contained By:
Masters Abstracts International85-01.
標題:
Software. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30516354click for full text (PQDT)
ISBN:
9798379871369
AI Empowered Program Performance Analysis.
Liang, Yuchen.
AI Empowered Program Performance Analysis.
- 1 online resource (27 pages)
Source: Masters Abstracts International, Volume: 85-01.
Thesis (M.Sc.)--North Carolina State University, 2023.
Includes bibliographical references
As software programs become increasingly complex, identifying and solving performance issues becomes more challenging. Dynamic profiling tools can help identify hotspots and bottlenecks, providing comprehensive and accurate information to developers. However, analyzing and processing the data collected by these tools can be complex and time-consuming. Additionally, diagnosing and optimizing performance problems requires a deep understanding of data structures, algorithms, and memory mechanisms.This thesis proposes a new tool combining multiple metric profiles and providing diagnostic reports and optimization recommendations. The key idea is to develop a rule for discovering the hot spot function, finding whether the problem is in the CPU, memory, or other aspects, and then producing prompts based on different optimization directions. With the help of large language model summaries and code capabilities, it helps us to produce optimized suggestions and code. Our tool would enable developers to quickly locate and solve performance problems in software programs.
Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023
Mode of access: World Wide Web
ISBN: 9798379871369Subjects--Topical Terms:
619355
Software.
Index Terms--Genre/Form:
542853
Electronic books.
AI Empowered Program Performance Analysis.
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As software programs become increasingly complex, identifying and solving performance issues becomes more challenging. Dynamic profiling tools can help identify hotspots and bottlenecks, providing comprehensive and accurate information to developers. However, analyzing and processing the data collected by these tools can be complex and time-consuming. Additionally, diagnosing and optimizing performance problems requires a deep understanding of data structures, algorithms, and memory mechanisms.This thesis proposes a new tool combining multiple metric profiles and providing diagnostic reports and optimization recommendations. The key idea is to develop a rule for discovering the hot spot function, finding whether the problem is in the CPU, memory, or other aspects, and then producing prompts based on different optimization directions. With the help of large language model summaries and code capabilities, it helps us to produce optimized suggestions and code. Our tool would enable developers to quickly locate and solve performance problems in software programs.
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