263-2910-00L  Program Analysis

Semester Spring Semester 2015
Lecturers M. Vechev
Periodicity yearly course
Language of instruction English


Abstract Modern program analysis techniques are the predominant approach for automatically reasoning about real world programs -- its techniques have been applied in a vast range of application domains.

The course provides an introduction to the fundamental principles, applications, and research trends of modern program analysis.

Objective The course has 4 main objectives:

* Understand the foundational principles behind program analysis techniques.

* Understand how to apply these principles to build practical, working analyzers for real world problems.

* Understand how to combine these techniques with other approaches (e.g. machine learning techniques) to build powerful end-to-end reasoning systems, not possible otherwise.

* Gain familiarity with the state-of-the-art in the area and the future research trends in the next 5-10 years.
Content The last decade has seen an explosion in modern program analysis techniques. These techniques are increasingly being used to reason about a vast range of computational paradigms including:

* finding security violations in web and mobile applications such as JavaScript and Android
* practical type checking and inference (e.g. Facebook's recently released Flow analyzer).
* combinations with machine learning techniques for learning from massive programming data guiding prediction of program properties and prediction of new code.
* establishing properties of biological systems (e.g. DNA computation)
* finding serious errors in systems software (e.g. Linux kernel, device drivers, file systems)
* automatic discovery of new algorithms (e.g. concurrent data structures, distributed algorithms) and end-user programming.
* compilers for domain specific languages
* architecture-driven reasoning of concurrent software (e.g. Intel's x86, ARM, IBM's Power).

This course will provide a comprehensive introduction to modern, state-of-the-art program analysis concepts, principles and research trends, including:

* Static & Dynamic Analysis:
- concepts: memory safety, type checking and inference, typestate, concurrency analysis, abstract interpretation (domains, soundness, precision, fixed points)
- frameworks: Valgrind, FastTrack, EventRacer, Apron, PPL, Facebook's Flow analyzer.

* Statistical program reasoning:
- concepts: combining analysis with statistical models (e.g. Language models, Bayesian networks, Neural networks, etc)
- frameworks: Slang, JSNice (http://jsnice.org)

* Predicate abstraction:
- concepts: Graf-Saidi, Boolean programs, lazy abstraction
- frameworks: Microsoft's SLAM for C programs, Fender

* Symbolic execution:
- concepts: SMT, concolic execution
- frameworks: S2E, KLEE, Sage

* Security Analysis:
- concepts: static + dynamic combination
- example: malware detection

* Pointer analysis:
- concepts: Andersen's, Steensgaard's analysis
- frameworks: Soot, LLVM, WALA

* Program synthesis:
- concepts: L*, version spaces, PBE, CEGIS
- frameworks: Sketch, AGS, SmartEdit, ReSynth

* Applications of Analysis & Synthesis:
- GPU programs, security errors, device drivers, concurrent algorithms, end-user programming.

To gain a deeper understanding of how to apply these techniques in practice, the course will involve a small hands-on programming project where based on the principles introduced in class, the students will build a program reasoning engine (e.g. analysis, predictions) for a modern programming language.
Lecture notes The lectures notes will be distributed in class.
Literature Distributed in class.
Prerequisites / Notice This course is aimed at both graduate (M.Sc., PhD) students as well as advanced undergraduate students.