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Research Projects

In order to produce effective fault-localization, debugging, failure-clustering, and test-suite maintenance techniques, researchers would benefit from a deeper understanding of how faults (i.e., bugs) behave and interact with each other. Some faults, even if executed, may or may not propagate to the output, and even still may or may not influence the output in a way to cause failure. Furthermore, in the presence of multiple faults, faults may interact in a way to obscure each other or in a way to produce behavior not seen in their isolation.

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Project Dates: 
August 2011

Previous studies have shown that there is a non-trivial amount of duplication in source code. We analyzed a corpus of 2.6 million non-fork projects hosted on GitHub representing over 258 million files written in Java, C++ Python and JavaScript. We found that this corpus has a mere 54 million unique files. In other words, 79% of the code on GitHub consists of clones of previously created files. There is considerable variation between language ecosystems. JavaScript has the highest rate of file duplication, only 7% of the files are distinct.

Project Dates: 
January 2017

We developed a fault-localization technique that utilized correlation-based heuristics. The technique and tool was called Tarantula.  Tarantula uses the pass/fail statuses of test cases and the events that occurred during execution of each test case to offer the developer recommendations of what may be the faults that are causing test-case failures. The intuition of the approach is to find correlations between execution events and test-case outcomes --- those events that correlate most highly with failure are suggested as places to begin investigation.

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Project Dates: 
May 2001

Savasana is the first white-box approach that uses code analysis for reasoning about consistency of adaptation.

Savasana consists of two parts: Static Code Analysis runs on the system's code and Run-time Control manages the corresponding running system.

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Project Dates: 
January 2016

DELDroid is an automated system for determination of least privilege architecture in Android and its enforcement at runtime. A key contribution of our approach is the ability to limit the privileges granted to apps without the need to modify them.

DELDroid utilizes static program analysis techniques to extract the exact privileges each component needs for providing its functionality. A Multiple-Domain Matrix representation of the system's architecture is then used to automatically analyze the security posture of the system and derive its least-privilege architecture.

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Project Dates: 
July 2016

We developed techniques for clustering of failures. Failure-clustering techniques attempt to categorize failing test cases according to the bugs that caused them. Test cases are clustered by utilizing their execution profiles (gathered from instrumented versions of the code) as a means to encode the behavior of those executions. Such techniques can offer suggestions for duplicate submissions of bug reports.

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Project Dates: 
July 2007

We developed a token-based approach for large scale code clone detection which is based on a filtering heuristic that reduces the number of token comparisons when the two code blocks are compared. We also developed a MapReduce based parallel algorithm that uses the filtering heuristic and scales to thousands of projects. The filtering heuristic is generic and can also be used in conjunction with other token-based approaches. In that context, we demonstrated how it can increase the retrieval speed and decrease the memory usage of the index-based approaches.

Project Dates: 
July 2011 to January 2014

To enable much of our research to enable program understanding, software quality, and maintenance, we utilize and develop analyses of program code. These analyses model the flows of information through the logic of programs and systems. With these analysis models enable automated techniques to assist development and maintenance tasks.

Research Area(s): 
Project Dates: 
March 1998

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