Lecture 10. Dynamic Analysis
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1 Lecture 10. Dynamic Analysis Wei Le Thank Xiangyu Zhang, Michael Ernst, Tom Ball for Some of the Slides Iowa State University
2 Outline What is dynamic analysis? Instrumentation Analysis Representing dynamic information Dynamic slicing Applications: specification generation and Diakon
3 What is dynamic analysis Learn from executions: Run programs where are the test inputs? what are the coverage? what parts of the code should be run? how to create runtime environment? Collect data online or offline (log analysis)? what is the overhead (memory and performance) how to capture and replay the data: compress, statistical approach Analyze data dynamic slicing model-based statistical and machine learning approaches (an interesting discussion on comparing the two:
4 What is the meaning of Run? Abstract interpretation and static analyses run a program over an abstract domain: OUT=F(IN,s) Dynamic analysis: abstraction used in parallel with, not in place of, concrete values: OUT=F(IN, si, v)
5 How dynamic analysis is used? Profiling-guided code optimization: which paths are frequently executed? Performance analysis Mining program invariants, protocols Fault localization, debugging (replay concurrency bugs) Bug finding...
6 Running the programs The test suite determines the expense (in time and space) The test suite determines the accuracy (what executions are never seen) Coverage or frequency: statements, branches, paths, procedure calls, types, method dispatch Values computed: parameters, array indices Run time, memory usage
7 Instrumentation A technique that inserts extra code into a program to collect runtime information.
8 Instrumentation A technique that inserts extra code into a program to collect runtime information.
9 Instrumentation A technique that inserts extra code into a program to collect runtime information.
10 Source and Binary Instrumentation Source instrumentation: instrument source programs Binary instrumentation: instrument executables directly: No need to recompile or relink Discover code at runtime Handle dynamically-generated code Attach to running processes
11 Binary Instrumentation Tool Is Dominant Libraries are a big pain for source code level instrumentation e.g., Proprietary libraries: communication (MPI, PVM), linear algebra (NGA), database query (SQL libraries). Easily handle multi-lingual programs: source code level instrumentation is heavily language dependent. More complicated semantics: Turning off compiler optimizations can maintain a almost perfect mapping from instructions to source code lines Worms and viruses are rarely provided with source code
12 Static Instrumentation
13 Dynamic Instrumentation
14 Instrumentation Tools Source instrumentation tools: AIMS, Paradyn, Pablo... [1] Binary instrumentation tools: Static (insert in the code and then run): ATOM, EEL, Etch, Morph Dynamic (run while insert the instrumentation): Dyninst, Vulcan, DTrace, Valgrind, Strata, DynamoRIO, PIN
15 Valgrind See Valgrind slides
16 PIN See PIN slides
17 Instrumentation: a Summary Source-level: Pattern-matching over parse tree or AST and rewriting Full access to source information A* [Ladd, Ramming], Astlog [Crew], Binary-level: ATOM [Srivastava], EEL [Larus], Diablo, Bluto Analyze programs from multiple languages Typicaly done at some IR level Dynamic instrumentation (run-time): Valgrind, PIN Java instrumentation tools: ASM ( Small and fast, Supports Java 1.5 BCEL ( Works well, No longer under active development SERP ( Not designed for speed, Somewhat memory intensive Soot ( Supports source, Designed for optimizations
18 Design Your Instrumentation How much to generate? Everything Just the necessary facts Less than necessary On-line vs. off-line analysis What/When to instrument? Source code, IR, assembly, machine code Preprocessor, compile-time, link-time, executable, run-time
19 Control Flow Trace
20 Dynamic Dependence Graph
21 Whole Execution Trace
22 Dynamic Slicing See Xiangyu s slides
23 Specification Generation-Diakon Daikon infers invariants from a program trace Looks for invariants between each combination of variables Polynomial in the number of variables Easy to implement offline, first pass finds equal variables More complex to implement incrementally.
24 Specification Generation-Diakon
25 Specification Generation
26 Specification Generation
27 Data Analysis Techniques Data mining (mining database) fields, related to information retrieval, text mining Associate rules mining Sequential patterns mining Beyond: Structure patterns (tree, graphs) mining Definition: what is it? Which algorithm? there exist off-the-shelf packages
28 Associate Rules Mining Proposed by Agrawal et al in Initially used for Market Basket Analysis to find how items purchased by customers are related: Bread Milk [sup = 5%, conf = 100%] Applications: Object, properties patient / symptoms movies / ratings web pages / keywords basket / products what are examples in computing?
29 The Model: Data
30 The Model: Rules
31 Algorithms and Extensions Apriori Eclat FP-growth Relevant concepts: Frequent itemset mining Maximal itemset mining [Bayardo, 1998] Closed itemset mining [Pasquier, 1999]
32 Patterns in Sequences Substrings Regular expressions Partial orders Structured patterns, directed acyclic graphs (mining for frequent subgraphs) Episodes Constraint-based sequential pattern mining (e.g., the item satisfies certain values)
![Sequential Pattern Mining [2] Sequential pattern mining: given a](/docs-images/81/83666968/images/33-0.jpg "set of sequences, find the complete set of frequent subsequences")
33 Sequential Pattern Mining [2] Sequential pattern mining: given a set of sequences, find the complete set of frequent subsequences
34 Sequential Mining Algorithms
35 Software C++ Implementations of Apriori, Eclat, FP-growth and several other algorithms are available on goethals/software/ and on
36 Designing Your Dynamic Analysis Dynamic analysis is, at its heart, an experimental effort: Have insight Build tool Evaluate efficiency and effectiveness Rethink
37 Jonathan Chen. extensible source-level instrumentation tool for performance measurement. Technical report, Rensselaer Polytechnic Institute, Carl H. Mooney and John F. Roddick. Sequential pattern mining approaches and algorithms. ACM Comput. Surv., 45(2):19:1 19:39, March 2013.
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