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New Results in Program Slicing

New Results in Program Slicing. Aharon Abadi, Ran Ettinger, and Yishai Feldman IBM Haifa Research Lab. Context. The Programmer’s Apprentice The Plan Calculus Bogart Midas Sliding Painless Paz Aderet. Improving Slice Accuracy by Compression of Data and Control Flow Paths.

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New Results in Program Slicing

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  1. New Results inProgram Slicing Aharon Abadi, Ran Ettinger, and Yishai Feldman IBM Haifa Research Lab

  2. Context • The Programmer’s Apprentice • The Plan Calculus • Bogart • Midas • Sliding • Painless • Paz • Aderet

  3. Improving Slice Accuracy by Compression of Data and Control Flow Paths Presented at ESEC/FSE 2009

  4. Program Slicing Program Slice x := exp x := exp Start Slice The same sequence of values

  5. Control-Flow Path Compression test X test X if-zero-go-to A if-zero-go-to A . . . L:test Y if-zero- go-to B go-to B . . . go-to L A:Z0 A:Z0 • Work in two stages: • Compute the ‘traditional’ slice • Control dependences • Data Dependences • - Compute the necessary branches • to prevent infeasible control paths . . . B: B:

  6. Control-Flow Path Compression test X test X test X if-zero-go-to A if-zero-go-to A go-to B . . . • Limitations of previous approaches: • - insert all the loop; • add branches not from the program; • or • - do not preserve behavior A:Z0 L:test Y B: if-zero- go-to B go-to B . . . go-to L • This algorithm: • preserves behavior • yields a sub-program • one version may turn conditional branches into unconditional ones (“rhetorization”) A:Z0 . . . B:

  7. Data-Flow Path Compression Start:R2:=0 Start:R2:=0 R7:=exp1 Loop: R2:=R2 + 1 compare R2, R9 if-not-less-go-to Out Temp:=R7 R7:=Temp go-to Loop Out: R0:=R7 + 1 R7:=exp1 Loop: R2:=R2 + 1 compare R2, R9 R7:=exp1 Out: R0:=R7 + 1 if-not-less-go-to Out use R7 Temp:=R7; spill R7 to memory … ; code that uses ; all registers The result is too large R7:=Temp; restore R7 The value of R7 does not depend on the loop go-to Loop Out: R0:= R7 + 1 Previous syntax-preserving algorithms insert the loop and the assignments inside it

  8. Control-Flow Path Compression if (x<11) goto A4 x<11 F T x := x+1 goto A4 x:=x+1 goto A2 A4: if (x<9) goto A3 x<9 T F goto A2 x := x-1 goto A3 A1: if (y<T) goto A2 x:=x+2 x:=x-1 y := y–1 goto A1 y<T T F A3: x := x+2 goto A2 A2: print(x) print(x) y:=y-1 goto A

  9. Compute the ‘Traditional’ Slice if (x<11) goto A4 x<11 x<11 F T x := x+1 goto A4 x:=x+1 x:=x+1 goto A2 A4: if (x<9) goto A3 x<9 x<9 goto A2 T F x := x-1 goto A3 A1: if (y<T) goto A2 x:=x+2 x:=x+2 x:=x-1 x:=x-1 y := y–1 goto A1 y<T y<T T F A3: x := x+2 goto A2 A2: print(x) print(x) print(x) y:=y-1 goto A

  10. Completing Control Flow Paths:Main Lemma All paths from the same point in the slice enter the slice at a single point • precisely identifies the possible sets of branches that • may be added to the slice • any path in the original program can be chosen • optimizations can be performed

  11. Compute the Necessary Branches x<11 F T if(x<11) goto A4 x:=x+1 goto A4 x:=x+1 goto A2 goto A2 x<9 T F A4: if(x<9) goto A3 x:=x-1 goto A3 A1: if(y<T) goto A2 x:=x+2 x:=x-1 y:=y–1 goto A1 y<T T F A3: x:=x+2 goto A2 A2: print(x) print(x) y:=y-1 goto A

  12. Data-Flow Path Compression Start:R2:=0 R7:=exp1 Loop: R2:=R2 + 1 compare R2, R9 if-not-less-go-to Out use R7 Temp:=R7; spill R7 to ;memory … ; code that uses ;all registers R7:=Temp; restore R7 go-to Loop Out: R0:=R7 + 1 R2:=0 R7:=exp1 R2:=R2+1 compare R2,R9 if-not-less use R7 go-to Out Temp:=R7 R7:=exp1 Out:R0:=R7 + 1 +1 R0:=R7+1 exit R7:=Temp goto Loop

  13. out data port holds the last value in data port holds the next value Data-Flow Path Compression Start:R2:=0 R7:=exp1 Loop: R2:=R2 + 1 compare R2, R9 if-not-less-go-to Out use R7 Temp:=R7; spill R7 to ; memory … ; code that uses ; all registers R7:=Temp; restore R7 go-to Loop Out: R0:=R7+1 R2:=0 0 d1 d2 exp1 R7:=exp1 d1 R2:=R2+1 ++ compare R2,R9 if-not-less use R7 go-to Out Temp:=R7 • The Plan Calculus:The Programmer’s Apprentice,Rich and Waters, 1990 ++ R7:=R7+1 • R7,Temp carry the value of exp1 exit R7:=Temp • Use data edges instead of variables goto-Loop

  14. compare R2,R9 F T exit entry Start:R2:=0 Loop: R2:=R2 + 1 compare R2, R9 if-not-less-go-to Out use R7 Temp:=R7; spill R7 to ; memory … ; code that uses ; all registers R7:=Temp; restore R7 go-to Loop R7:=exp1 R7:=exp1 exp1 0 R2 R9 R2 ++ R7 Out: R0:=R7 + 1 Out: R0:=R7 + 1 if-not-less R7:= exp1 R0:=R7 + 1 use R7 ++ R0

  15. compare R2,R9 F T exit Decompression entry Start:R2:=0 Loop:R2:=R2 + 1 compare R2, R9 if-not-less- use R7 ; spill R7 to ; memory … ; code that uses ; all registers ; restore R7 go-to Loop R7:=exp1 exp1 0 go-to Out R2 R9 Temp:=R7 R2 ++ R7 R7:=Temp Out: R0:=R7 + 1 if-not-less use R7 R7:=exp1 go-to Out ++ R0:=R7 + 1 Out: R0

  16. Properties of the Slices • Syntax preserving, possibly rhetorizing • Behavior preserving • Executable • For structured programs • At least as accurate as previous algorithms • Strictly smaller in interesting cases • For unstructured programs • Empirically shown to be superior • Modification of the algorithm guaranteed at least as accurate

  17. Implementation • A family of slicing algorithms • rhetorizing (*RB, *RM) • strictly syntax-preserving (*PB, *PM) • amorphous (*AB, *AM) • adds new branches(not from the program) test X if-zero-go-to A . . . goto A2 A1:if(y<T) L:test Y if-zero-go-to B . . . go-to L B:go-to C go-to exit A:Z  0 C: . . . goto exit

  18. Empirical Study • Corpus of 15 manually-written assembly-language modules from a large mainframe product • 8578 non-comment source lines • Computed slices from all lines • 5801 non-empty slices

  19. Empirical Results

  20. Related Work BH: Ball & Horwitz 1993 CF: Choi & Ferrante 1994 Ag: Agrawal 1994 KH: Kumar & Horwitz 2002 HD: Harman & Danicic 1998 HLB: Harman, Lakhotia & Binkley 2006

  21. Conclusions • Two techniques for reducing slice size • Control-Flow Path Compression • Precise identification of all correct solutions • Shortest paths significantly improve slice accuracy • 17-22% improvement for 30-37% of the cases • Data-Flow Path Compression • Eliminates copy assignments • Yields significant improvement in a few cases • 24% improvement for 1% of the slices computed • Strictly smaller even for structured programs

  22. Fine Slicing forProgram Transformation

  23. Refactoring’s Rubicon:Extract Method • Automating Extract Method is Refactoring’s Rubicon (Fowler*) • The one that demonstrates “serious tool support” • Precondition for many other transformations • This Rubicon has not yet been crossed • Getting it right requires more analysis capability than is available in current IDEs *http://www.martinfowler.com/articles/refactoringRubicon.html

  24. Fowler’s Example (website) void printOwing() { printBanner(); //print details System.out.println("name: " + _name); System.out.println("amount " + getOutstanding()); } void printOwing() { printBanner(); printDetails(getOutstanding()); } void printDetails(double outstanding) { System.out.println("name: " + _name); System.out.println("amount " + outstanding); }

  25. A Case Study inEnterprise Refactoring • Converted a Java Servlet to use the MVC pattern* • Used as much automated support as available • The whole conversion could be described as a series of cataloged (“small”) refactorings • Most steps were inadequately supported by the IDE • Some were not supported at all * Based on Alex Chaffee’s “Refactoring to Model-View-Controller” article (http://www.purpletech.com/articles/mvc/refactoring-to-mvc.html)

  26. Fully Supported Refactorings Uses Extract Method Extract Temp (Self) Encapsulate Field Replace Magic Number with Symbolic Constant Inline Temp Extract Superclass Delete Methods Move Method 3 3 2 1 1 1 1 1 Total 13 Case-Study: Automation (1)

  27. Partial(*) or No(**) Support Uses Extract Method * Substitute Expression ** Replace Temp with Query * Replace Method with Method Object ** Substitute Statement ** Extract Class * Move Statement (or Swap Statements) ** 10 5 3 2 1 1 1 Total 23 Case-Study: Automation (2)

  28. Currently Unsupported Casesof Extract Method • Extract multiple fragments • Extract a partial fragment • select sub-expressions as parameters • Extract loop with partial body • loop duplication with data flow • Extract code with conditional exits Program slicing pulls related code together!

  29. Traditional Slicing slice (v.): to cut with or as if with a knife slice (n.): a thin flat piece cut from something Fine Slicing Merriam-Webster

  30. Traditional Slicing A (backward) slice of a given program with respect to selected “interesting” variables is a subprogram that computes the same values as the original program for the selected variables A (backward) fine sliceof a given program with respect to selected “interesting” variables and other “oracle” variables is a subprogram that computes the same values as the original program for the selected variables, given values for the oracle variables Fine Slicing

  31. Fine Slicing • A generalization of traditional program slicing • Fine slices can be precisely bounded • Slicing criteria include set of data and control dependences to ignore • Fine slices are executable and extractable • Complement slices (co-slices) are also fine slices • Oracle-based semantics for fine slices • Algorithm for computing data-structure representing the oracle • Forward fine slices are executable, may be slightly larger than traditional forward slices • Confines generalize blocks for unstructured programs

  32. Extract Computation • A new refactoring • Extracts a fine slice into contiguous code • Computes the co-slice • Computation can then be extracted into a separate method using Extract Method • Passes necessary “oracle” variables between slice and co-slice • Generates new containers if series of values need to be passed

  33. (a) Extract multiple fragments User user = getCurrentUser(request); if (user == null) { response.sendRedirect(LOGIN_PAGE_URL); return; } response.setContentType("text/html"); disableCache(response); String albumName = request.getParameter("album"); PrintWriter out = response.getWriter();

  34. (b) Extract a partial fragment out.println(DOCTYPE_HTML); out.println("<html>"); out.println("<head>"); out.println("<title>Error</title>"); out.println("</head>"); out.print("<body><p class='error'>"); out.print("Could not load album '" + albumName + "'"); out.println("</p></body>"); out.println("</html>");

  35. (c) Extract loop with partial body 1 2 3 4 5 6 7 8 9 10 out.println("<table border=0>"); int start = page * 20; int end = start + 20; end = Math.min(end, album.getPictures().size()); for (int i = start; i < end; i++) { Picture picture = album.getPicture(i); printPicture(out, picture); } out.println("</table>");

  36. 2 3 4 5 *** *** 6 7 *** 9 1 6 8 10 int start = page * 20; int end = start + 20; end = Math.min(end, album.getPictures().size()); Queue<Picture> pictures = new LinkedList<Picture>(); for (int i = start; i < end; i++) { Picture picture = album.getPicture(i); pictures.add(picture); } out.println("<table border=0>"); for (int i = start; i < end; i++) printPicture(out, pictures.remove()); out.println("</table>");

  37. (d) Extract code with conditional exits if (album == null) { new ErrorPage("Could not load album '" + album.getName() + "'").printMessage(out); return; } //...

  38. if (invalidAlbum(album, out)) return; } //... boolean invalidAlbum(Album album, PrintWriter out) { boolean invalid = album == null; if (invalid) { new ErrorPage("Could not load album '" + album.getName() + "'").printMessage(out); } return invalid; }

  39. entry "<table border=0>" Token Semantics out out.println("<table border=0>"); int start = page * 20; int end = start + 20; end = Math.min(end, album.getPictures().size()); for (int i = start; i < end; i++) { Picture picture = album.getPicture(i); printPicture(out, picture); } out.println("</table>"); println page 20 * start + out album out getPictures i size end min getPicture p2 p1 end "</table>" i out out p1 p2 < println printPicture T F exit ++

  40. entry printPicture "<table border=0>" Fine Slicing out out.println("<table border=0>"); int start = page * 20; int end = start + 20; end = Math.min(end, album.getPictures().size()); for (int i = start; i < end; i++) { Picture picture = album.getPicture(i); printPicture(out, picture); } out.println("</table>"); println page 20 * start + out album out getPictures i size end min getPicture end "</table>" i out out < println printPicture T F exit ++

  41. entry printPicture "<table border=0>" The Fine Slice out out.println("<table border=0>"); for (int i = start; i < end; i++) { printPicture(out, picture); } out.println("</table>"); println start end out picture out i "</table>" i out out < println printPicture T F exit ++

  42. printPicture entry "<table border=0>" Co-Slicing out out.println("<table border=0>"); int start = page * 20; int end = start + 20; end = Math.min(end, album.getPictures().size()); for (int i = start; i < end; i++) { Picture picture = album.getPicture(i); printPicture(out, picture); } out.println("</table>"); println page 20 * start + out album out getPictures i size end min getPicture end "</table>" i out out < println printPicture T F exit ++

  43. entry The Co-Slice int start = page * 20; int end = start + 20; end = Math.min(end, album.getPictures().size()); for (int i = start; i < end; i++) { Picture picture = album.getPicture(i); } page 20 * start + album getPictures i size end min getPicture i start picture < out end T F exit ++

  44. entry entry printPicture Co-slice Fine slice page 20 "<table border=0>" * out start println + album end start picture getPictures i end size min getPicture picture i start i out < "</table>" < end out T F println T F ++ out ++ exit exit

  45. entry out.println("<table border=0>"); int start = page * 20; int end = start + 20; end = Math.min(end, album.getPictures().size()); Queue<Picture> pictures = new LinkedList<Picture>(); for (int i = start; i < end; i++) { Picture picture = album.getPicture(i); pictures.add(picture); printPicture(out,pictures.remove()); } out.println("</table>"); "<table border=0>" Adding a Container out println println page 20 * start + out album out getPictures i pictures size end min getPicture new pictures picture add pictures end pictures "</table>" i out out remove remove picture < < println println printPicture printPicture T F exit ++ ++

  46. voiddisplay(PrintStream out, int start, int end, Queue<Picture>pictures){ out.println("<table border=0>"); for (int i = start; i < end; i++) { printPicture(out, pictures.remove()); } out.println("</table>"); } entry entry "<table border=0>" The Fine Slice out println println end start pictures out out i pictures pictures "</table>" i out out remove remove picture < < println println printPicture printPicture T F exit ++ ++

  47. out.println("<table border=0>"); int start = page * 20; int end = start + 20; end = Math.min(end, album.getPictures().size()); Queue<Picture> pictures = new LinkedList<Picture>(); for (int i = start; i < end; i++) { Picture picture = album.getPicture(i); pictures.add(picture); printPicture(out,pictures.remove()); } out.println("</table>"); entry "<table border=0>" Program with Container out println println page 20 * start + out album out getPictures i pictures size end min getPicture new pictures picture add pictures end pictures "</table>" i out out remove remove picture < < println println printPicture printPicture T F exit ++ ++

  48. entry int start = page * 20; int end = start + 20; end = Math.min(end, album.getPictures().size()); Queue<Picture> pictures = new LinkedList<Picture>(); for (int i = start; i < end; i++) { Picture picture = album.getPicture(i); pictures.add(picture); } display(out, start, end, pictures); The Co-Slice out page 20 * start + album getPictures start i pictures size end min getPicture new pictures picture add pictures end pictures pictures i < < display out T F exit ++ ++

  49. Conclusions • Fine slicing algorithm yields executable slices whose boundaries can be precisely controlled • Can be used to make any subset of a program executable by adding some control structures but not the data on which they depend • including forward slices, thin slices, barrier slices, chops, and barrier chops • Conjecture: the size of these executable programs will not be substantially larger

  50. Conclusions • New Extract Computation refactoring is an important step towards the automation of Extract Method in difficult cases • Enables the automation of big refactorings from smaller building blocks • Uses new fine-slicing algorithm • Automatically computes complement slices (co-slices) • Automatically generates containers to pass series of values if necessary

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