Amorphous Program Slicing through Partial Evaluation
This presentation by David Binkley and Kyle Sluder from Loyola College in Maryland explores the concept of amorphous slicing in program analysis. Unlike traditional static and dynamic slicing methods, amorphous slicing does not preserve syntax and offers distinct advantages in understanding program behavior. The talk covers the fundamentals of partial evaluation, its application in amorphous slicing, and presents results demonstrating its efficacy in identifying computational flaws in programs. Future work will focus on exploring further examples and the relationship between conditioned slicing and static variables.
Amorphous Program Slicing through Partial Evaluation
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Presentation Transcript
Amorphous Program Slicing through Partial Evaluation David Binkley & Kyle Sluder Loyola College in Maryland4501 N. Charles St.Baltimore, MD 21210
Agenda • Background • Fundamentals • Method • Results • Conclusions & Future Work
Traditional slicing Projection of program given criterion Criterion: (line #, {variables}) Preserves syntax int foo() { int x, y, z; x = 20; y = x + 10; z = 30; return x + y + z; } Background Criterion: (6, {z})
Background • Traditional slicing domains • Static • Not concerned with program’s input • Dynamic • Modifies slice based on input
Amorphous slicing Not syntax-preserving (biggest advantage) No distinction between static and dynamic int foo() { int x, y, z; x = 20; y = x + 10; z = 30; return x + y + z; } Background • int foo() • { • int z = 30; • return z; • }
Background Specialized for input All 1 Traditional static slicing Traditional dynamic slicing Yes Syntax Preservation Amorphous slicing (N/A) No
Background • Applications of Slicing • Debugging: original use • Program proving • Refactoring • Program/algorithm analysis
Background • What is partial evaluation? • Applications • Optimization • Program (pre)compilation • Refactoring • Program proving/analysis
Fundamentals • Based on similarity of partial evaluation and amorphous slicing • Foundation (Harman et al.) • Application (Silva and Vidal) • Applied to C • No longer in the realm of functional programming • A lot harder to do successfully
Pre-processing Use TXL to extract variables to global scope Partially evaluate Prepare configuration file (provide criterion) Run Tempo Slice Use CodeSurfer Post-process Use TXL for cleanup Method
Computation example Program needs to compute statistics on array of integers Program exhibits bug in average Results
Results • Traditional slice not helpful • Syntactic-preservation constraint inhibits efficacy • Only shows what is already known: biggest is not involved in computation of average • Amorphous slice helpful • Shows that average is not built correctly: does not rely on sum, despite its presence in original program • Sum must therefore not be receiving the right value
Results • Safety slicing • Array bounds safety • Artificially insert assignments to variable “safe” before array access • Assert safe = T at end
Conclusions and Future Work • Amorphous slicing is possible for C • Partial evaluation is very viable method for achieving that goal • Need more examples of efficacy • Explore relationship between conditioned slicing and “static” variables from P.E. perspective This work is supported by National Science Founda- tion grant CCR0305330. Kyle Sluder is supported by the Loyola College in Maryland Hauber Fellowship.
