Generalized techniques for using system execution traces to support software performance analysis

<p>This dissertation proposes generalized techniques to support software performance analysis using system execution traces in the absence of software development artifacts such as source code. The proposed techniques do not require modifications to the source code, or to the software binaries, for the purpose of software analysis (non-intrusive). The proposed techniques are also not tightly coupled to the architecture specific details of the system being analyzed. This dissertation extends the current techniques of using system execution traces to evaluate software performance properties, such as response times, service times. The dissertation also proposes a novel technique to auto-construct a dataflow model from the system execution trace, which will be useful in evaluating software performance properties. Finally, it showcases how we can use execution traces in a novel technique to detect Excessive Dynamic Memory Allocations software performance anti-pattern. This is the first attempt, according to the author's best knowledge, of a technique to detect automatically the excessive dynamic memory allocations anti-pattern. The contributions from this dissertation will ease the laborious process of software performance analysis and provide a foundation for helping software developers quickly locate the causes for negative performance results via execution traces.</p>

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computer Science

Year

2015

Author dc:creator

• Peiris, Thelge Manjula

Contributors dc:contributor

• James H. Hill
• Mohammad Al Hasan
• Hubert Dunsmore
• Xiangyu Zhang

• Applied sciences
• Adapting
• Anti-pattern
• Dataflow models
• Dynamic binary instrumentation
• Performance
• System execution traces
• Computer Engineering
• Computer Sciences

Repository record dc:identifier

https://docs.lib.purdue.edu/open_access_dissertations/693

OAI identifier oai:identifier

oai:docs.lib.purdue.edu:open_access_dissertations-1836