Title

Automated Fitness Guided Fault Localization

Presenter Information

Alex Bertels

Department

Computer Science

Major

Computer Science

Research Advisor

Tauritz, Daniel R.

Advisor's Department

Computer Science

Funding Source

Missouri S&T Opportunities for Undergraduate Research Experiences (OURE) Program

Abstract

For a given computer program, logical flaws produce invalid results which can be difficult, and thus costly, to correct. Finding software errors is a significant step towards fixing them. Given this, having a tool to automatically identify the most likely parts of the program containing these faults would be a huge advantage. Classical fault localization tools require an “oracle,” typically a human expert, to determine if the program is working as intended. The research presented here novelly employs a fitness function which automates the process by computationally enforcing the program specifications. The current Fitness Guided Fault Localization (FGFL) system combines two techniques: (1) trace comparison, and (2) trend-based line suspicion. Two additional techniques are under investigation, but require additional research to overcome their limitations, called: (1) run-time fitness monitor, and (2) critical slicing. Empirical comparisons are statistically analyzed for significance.

Biography

Alex is from Dorsey, Illinois. He is currently a sophomore in Computer Science, an Undergraduate Research Assistant in the Natural Computation Laboratory, a tutor for the Introduction to C++ labs (CS54), the Secretary for the Missouri S&T Association for Computing Machinery (ACM) Student Chapter, and will be interning this summer in the Center for Cyber Defenders at Sandia National Laboratories.

Research Category

Sciences

Presentation Type

Poster Presentation

Document Type

Poster

Location

Upper Atrium/Hallway

Presentation Date

10 Apr 2012, 9:00 am - 11:45 am

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Apr 10th, 9:00 AM Apr 10th, 11:45 AM

Automated Fitness Guided Fault Localization

Upper Atrium/Hallway

For a given computer program, logical flaws produce invalid results which can be difficult, and thus costly, to correct. Finding software errors is a significant step towards fixing them. Given this, having a tool to automatically identify the most likely parts of the program containing these faults would be a huge advantage. Classical fault localization tools require an “oracle,” typically a human expert, to determine if the program is working as intended. The research presented here novelly employs a fitness function which automates the process by computationally enforcing the program specifications. The current Fitness Guided Fault Localization (FGFL) system combines two techniques: (1) trace comparison, and (2) trend-based line suspicion. Two additional techniques are under investigation, but require additional research to overcome their limitations, called: (1) run-time fitness monitor, and (2) critical slicing. Empirical comparisons are statistically analyzed for significance.