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Introduction to Numerical Abstractions in Program Analysis

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This lecture explores numerical abstractions in program analysis, focusing on the inferencing of numeric properties of program variables such as integers and floating points. Key topics include non-relational and relational classifications, the role of equalities and inequalities, and the application of abstractions for detecting issues like division by zero and out-of-bound accesses. We will also cover constant propagation, intervals, and sound transformers, emphasizing the importance of weakly-relational abstractions and their practical aspects in program verification.

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Introduction to Numerical Abstractions in Program Analysis

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  1. Spring 2016Program Analysis and Verification Lecture 13: Numerical Abstractions Roman Manevich Ben-Gurion University

  2. Tentative syllabus

  3. Previously Composing abstract domains (and GCs) Widening and narrowing Interval domain

  4. Agenda • Abstractions for properties of numeric variables • Classification: • Relational vs. non-relational • Equalities vs. non-equalities • Zones

  5. Numerical Abstractions By Quilbert (own work, partially derived from en:Image:Poly.pov) [GPL (http://www.gnu.org/licenses/gpl.html)], via Wikimedia Commons

  6. Overview • Goal: infer numeric properties of program variables (integers, floating point) • Applications • Detect division by zero, overflow, out-of-bound array access • Help non-numerical domains • Classification • Non-relational • (Weakly-)relational • Equalities / Inequalities • Linear / non-linear • Exotic

  7. Implementation

  8. Non-relational abstractions

  9. Non-relational abstractions • Abstract each variable individually • Constant propagation [Kildall’73] • Intervals (Box) • Covered in previous lecture • Sign • Parity (congruences) • Zones

  10. Sign abstraction for variable x  neg pos 0  • Concrete lattice: C = (2State, , , , , State) • Sign = {, neg, 0, pos, } • GCC,Sign=(C, , , Sign) • Concretization • () = • (neg) = • (0) = • (pos) = • () = • Abstraction • ({17}) = • ({17, 0}) = • ({-1, 1}) = • How can we represent 0?

  11. Transformer x:=y*z Is it complete?

  12. Transformer x:=y*z Check at home: Abstract transformer is complete

  13. Transformer x:=y+z Is it complete?

  14. Transformer x:=y+z Check at home: Abstract transformer is not complete

  15. Parity abstraction for variable x  E O  Concrete lattice: C = (2State, , , , , State) Parity = {, E, O, } GCC,Parity=(C, , , Parity) () = ? (E) = ? (O) = ? () = ?

  16. Transformer x:=y+z

  17. Transformer x:=y+z

  18. Boxes (intervals) y 6 5 y  [3,6] 4 3 2 1 0 1 2 3 4 x • x  [1,4]

  19. Non-relational abstractions • Cannot prove properties that hold simultaneous for several variables • x = 2*y • x ≤ y

  20. Practical aspectsof Non-relational abstractions

  21. The abstraction • Abstract domain for variables x1,…,xn is the Cartesian product of a sub-domain for one variable D[x] • D[x1]  …  D[xn] • Need to implement join, meet, widening, narrowing just for sub-domain • Usually a non-relational is associated with a Galois Insertion • No reduction required • The Cartesian product is a reduced product

  22. Sound assignment transformers Let remove(S, x) be the operation that removes the factoid associated with x from S Let factoid(S, x) be the operation that returns the factoid associated with x in S x := c# S = remove(S, x)  ({[xc]}) x := y# S = remove(S, x)  {factoid(S, y)[x/y]} x := y+c# S = remove(S, x)  {factoid(S, y)[x/y] + c} x := y+z# S = remove(S, x)  {factoid(S, y)[x/y] + factoid(S, z)[x/z]} x := y*c# S = remove(S, x)  {factoid(S, y)[x/y] * c} x := y*z# S = remove(S, x)  {factoid(S, y)[x/y] * factoid(S, z)[x/z]}

  23. Sound assumetransformers assumex=c# S = S  ({[xc]}) assumex<c# S = … assumex=y# S = S  {factoid(S, y)[x/y]}  {factoid(S, x)[y/x]} assumexc# S = if S  ({[xc]}) then  else S

  24. (Weakly-)relational abstractions

  25. Relational abstractions • Represent correlations between all program variables • Polyhedra • Linear equalities • When correlations exist only between few variables (usually 2) we say that the abstraction is weakly-relational • Linear relations example (discussed in class) • Zone abstraction (next) • Octagons • Two-variable polyhedra • Usually abstraction is defined as the reduced product of the abstract domain for any pair of variables

  26. Zone abstraction

  27. Zone abstraction [Mine] y 6 x ≤ 4 −x ≤ −1 y ≤ 3 −y ≤ −1 x − y ≤ 1 5 4 3 2 1 0 1 2 3 4 x Maintain bounded differences between a pair of program variables (useful for tracking array accesses) Abstract state is a conjunction of linear inequalities of the form x-yc

  28. Difference bound matrices x ≤ 4 −x ≤ −1 y ≤ 3 −y ≤ −1 x − y ≤ 1 Add a special V0 variable for the number 0 Represent non-existent relations between variables by + entries Convenient for defining the partial order between two abstract elements… =?

  29. Ordering DBMs x ≤ 4 −x ≤ −1 y ≤ 3 −y ≤ −1 x − y ≤ 1 M1 = x ≤ 5 −x ≤ −1 y ≤ 3 x − y ≤ 1 M2 = How should we order M1 M2?

  30. Joining DBMs x ≤ 4 −x ≤ −1 y ≤ 3 −y ≤ −1 x − y ≤ 1 M1 = x ≤ 2 −x ≤ −1 y ≤ 0 x − y ≤ 1 M2 = How should we join M1 M2?

  31. Widening DBMs x ≤ 4 −x ≤ −1 y ≤ 3 −y ≤ −1 x − y ≤ 1 M1 = x ≤ 5 −x ≤ −1 y ≤ 3 x − y ≤ 1 M2 = How should we widen M1M2?

  32. Potential graph x ≤ 4 −x ≤ −1 y ≤ 3 −y ≤ −1 x − y ≤ 1 V0 3 -1 -1 3 x y 1 Can we tell whether a systemof constraints is satisfiable? Can you define a semantic reduction? A vertex per variable A directed edge with the weight of the inequality Enables computing semantic reduction by shortest-path algorithms

  33. Semantic reduction for zones Apply the following rule repeatedlyx - y ≤ c y - z ≤ d x - z ≤ e x - z ≤ min{e, c+d} When should we stop? Theorem 3.3.4. Best abstraction of potential sets and zones m∗ = (Pot ◦ Pot)(m)

  34. Zones assignment transformers remove(S, x): removes the x-factoids from S factoid(S, x): returns all x-factoids in S x := c# S = remove(S, x)  …? x := y+c# S = remove(S, x)  …? x := -y# S = remove(S, x)  …? x := y-z# S = remove(S, x)  …? x := y+z# S = …?

  35. Zones assignment transformers remove(S, x): removes the x-factoids from S factoid(S, x): returns all x-factoids in S x := c# S = remove(S, x)  {x-V0≤c, V0-x≤c} x := y+c# S = remove(S, x)  {x-y≤c, y-x≤-c} x := -y# S = remove(S, x)  {x-V0≤c |V0-y≤c} {V0-x≤-c | y-V0≤c} x := y-z# S = remove(S, x)  {x≤c} wherec=min{c1-c2 | y-w≤c1, z-w≤c2} x := y+z# S = x := y-t#(t := -z# S)

  36. More numerical domains

  37. Octagon abstraction [Mine-01] • captures relationships common in programs (array access) Abstract state is an intersection of linear inequalities of the form x yc

  38. Some inequality-basedrelational domains policy iteration

  39. What is the polyhedron abstraction? y x How do we abstract a circle?

  40. Equality-based domains • Simple congruences [Granger’89]: y=a mod k • Linear equalities [Karr’76]: a1*x1+…+ak*xk = c • Polynomial equalities:a1*x1d1*…*xkdk + b1*y1z1*…*ykzk+ … = c • Some good results are obtainable whend1+…+dk < n for some small n

  41. Exercise: 2-linear relations Infer linear relations between pairs of variables: y=a*x+b Handout

  42. see you next time

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