Program analysis tools

1998 ◽  
Vol 2 (1) ◽  
pp. 6-12 ◽  
Author(s):  
Chris Hankin
Keyword(s):  
Program Analysis ◽  
Analysis Tools

Productive Development of Dynamic Program Analysis Tools with DiSL

2013 ◽  
Author(s):  
Aibek Sarimbekov ◽  
Yudi Zheng ◽  
Danilo Ansaloni ◽  
Lubomir Bulej ◽  
Luka Marek ◽  
...  
Keyword(s):  
Program Analysis ◽  
Dynamic Program ◽  
Analysis Tools ◽  
Dynamic Program Analysis ◽  
Productive Development

Program Analysis Tools in Automated Grading of Homework Assignments

2018 ◽  
Author(s):  
Ana Milanova ◽  
Barbara Cutler ◽  
Buster Holzbauer ◽  
Evan Maicus ◽  
Samuel Breese ◽  
...  
Keyword(s):  
Program Analysis ◽  
Analysis Tools ◽  
Automated Grading

Combining Static Code Analysis and Machine Learning for Automatic Detection of Security Vulnerabilities in Mobile Apps

2018 ◽  
pp. 1121-1147
Author(s):  
Marco Pistoia ◽  
Omer Tripp ◽  
David Lubensky
Keyword(s):  
Machine Learning ◽  
Static Analysis ◽  
Private Information ◽  
Program Analysis ◽  
Mobile Applications ◽  
Mobile Apps ◽  
Security Vulnerabilities ◽  
Code Analysis ◽  
Analysis Tools ◽  
Static Code Analysis

Mobile devices have revolutionized many aspects of our lives. Without realizing it, we often run on them programs that access and transmit private information over the network. Integrity concerns arise when mobile applications use untrusted data as input to security-sensitive computations. Program-analysis tools for integrity and confidentiality enforcement have become a necessity. Static-analysis tools are particularly attractive because they do not require installing and executing the program, and have the potential of never missing any vulnerability. Nevertheless, such tools often have high false-positive rates. In order to reduce the number of false positives, static analysis has to be very precise, but this is in conflict with the analysis' performance and scalability, requiring a more refined model of the application. This chapter proposes Phoenix, a novel solution that combines static analysis with machine learning to identify programs exhibiting suspicious operations. This approach has been widely applied to mobile applications obtaining impressive results.

Combining Static Code Analysis and Machine Learning for Automatic Detection of Security Vulnerabilities in Mobile Apps

2017 ◽  
pp. 68-94
Author(s):  
Marco Pistoia ◽  
Omer Tripp ◽  
David Lubensky
Keyword(s):  
Machine Learning ◽  
Static Analysis ◽  
Private Information ◽  
Program Analysis ◽  
Mobile Applications ◽  
Mobile Apps ◽  
Security Vulnerabilities ◽  
Code Analysis ◽  
Analysis Tools ◽  
Static Code Analysis

Mobile devices have revolutionized many aspects of our lives. Without realizing it, we often run on them programs that access and transmit private information over the network. Integrity concerns arise when mobile applications use untrusted data as input to security-sensitive computations. Program-analysis tools for integrity and confidentiality enforcement have become a necessity. Static-analysis tools are particularly attractive because they do not require installing and executing the program, and have the potential of never missing any vulnerability. Nevertheless, such tools often have high false-positive rates. In order to reduce the number of false positives, static analysis has to be very precise, but this is in conflict with the analysis' performance and scalability, requiring a more refined model of the application. This chapter proposes Phoenix, a novel solution that combines static analysis with machine learning to identify programs exhibiting suspicious operations. This approach has been widely applied to mobile applications obtaining impressive results.

M09---Program analysis tools for massively parallel applications

2006 ◽  
Author(s):  
Andreas Knuepfer ◽  
Dieter Kranzlmueller ◽  
Bernd W. Mohr ◽  
Wolfgang E. Nagel
Keyword(s):  
Program Analysis ◽  
Parallel Applications ◽  
Massively Parallel ◽  
Analysis Tools
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