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Tao Xie University of Illinois at Urbana-Champaign Part of the research work described in this talk was done in collaboration with the Pex team (Nikolai.

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Presentation on theme: "Tao Xie University of Illinois at Urbana-Champaign Part of the research work described in this talk was done in collaboration with the Pex team (Nikolai."— Presentation transcript:

1 Tao Xie University of Illinois at Urbana-Champaign Part of the research work described in this talk was done in collaboration with the Pex team (Nikolai Tillmann, Peli de Halleux, et Research, ASE, and other collaborators; part of the research work was done by the Pex team only

2  Human  Expensive, incomplete, …  Brute Force  Pairwise, predefined data, etc…  Tool Automation!!

3 Running Symbolic PathFinder... … ===================================== ================= results no errors detected ===================================== ================= statistics elapsed time: 0:00:02 states: new=4, visited=0, backtracked=4, end=2 search: maxDepth=3, constraints=0 choice generators: thread=1, data=2 heap: gc=3, new=271, free=22 instructions: 2875 max memory: 81MB loaded code: classes=71, methods=884 … 3

4  Pex (released on May 2008)  30,388 download# (20 months, Feb 08-Oct 09)  Active user community: 1,436 forum posts during ~3 years (Oct 08- Nov 11)  Moles (released on Sept 2009)  Shipped with VS 12 as Fakes  “Provide Microsoft Fakes w/ all Visual Studio editions” got 1,457 community votes  Code Digger (released on Oct 2008 for VS 08/10, on Apr 2013 in VS Gallery for VS 12)  22,466 download# (10 months, Apr 13-Jan 14)

5  “Great tool to generate unit tests for parameter boundary tests. I like to see it integrated into Visual Studio and the testing features as far as in ReSharper! :)”  “What an awesome tool.. Help us to explore our logic by providing accurate input parameter for each logic branch.. You should try this as one of your ultimate tool :) It really saves a lot of our time to explore every logic branch in our apps..”

6  “What a fantastic tool. Whilst it’s not bullet proof, it shows amazing promise. I ran the Code Digger over a number of real-world methods and it immediately identified dozens of edge cases we hadn’t thought of. This is getting rolled-out to my team TODAY! Well done. Brilliant. Really brilliant.”  “Top stuff here. Very anxious for more of the Pex features that were available in VS 2010 Pex & Moles (like auto-gen unit tests). This tool is poised to become indispensable for anyone writing solid suites of unit tests.”

7 1,462,489 clicked 'Ask Pex!'

8 Secret Implementation class Secret { public static int Puzzle(int x) { if (x <= 0) return 1; return x * Puzzle(x-1); } Player Implementation class Player { public static int Puzzle(int x) { return x ; } class Test { public static void Driver(int x) { if (Secret.Puzzle(x) != Player.Puzzle(x)) throw new Exception(“Mismatch”); } behavior Secret Impl == Player Impl 8

9 “It really got me *excited*. The part that got me most is about spreading interest in teaching CS: I do think that it’s REALLY great for teaching | learning!” “I used to love the first person shooters and the satisfaction of blowing away a whole team of Noobies playing Rainbow Six, but this is far more fun.” “I’m afraid I’ll have to constrain myself to spend just an hour or so a day on this really exciting stuff, as I’m really stuffed with work.” X

10 https://www.codehunt.com/

11  August 8 – 11, 2013  300+ teams wrote tools to synthesize bit-vector programs  These tools were evaluated on a set of 1,800 benchmark problems  Main goal:  How would the top-teams fare against the best SMT solutions?

12 GAME PLAYER Can you tell me what A(16), A(42), A(128) are? I have a secret program A. Can you guess what it is? You have 5 minutes. A(16)=17, A(42)=43, A(128)=129. Ah. I bet A = λ x. x+1 Let me check … Nope. A(9)=9. Hmm. Ok, so what is A(11) and A(12) then? Since you ask so nicely: A(11)=12 and A(12)=13 Ah ha! I guess A = λ x. if x & 1 = 0 then x else x + 1 Let me check … Yep! That's right! You score one point. query.smt2 A ≈ λ x. x+1 ? query.smt2 A ≈ λ x. x+1 ? Yes! No! Counterexample: A(9) <> ( λ x.x+1) 9 query.smt2 A ≈ λ x. if x&1=0…? query.smt2 A ≈ λ x. if x&1=0…? ICFP Programming Contest

13  NOT Random:  Cheap, Fast  “It passed a thousand tests” feeling  …  But Dynamic Symbolic Execution: e.g., Pex, CUTE,EXE  White box  Constraint Solving

14 Code to generate inputs for: Constraints to solve a!=null a!=null && a.Length>0 a!=null && a.Length>0 && a[0]== void CoverMe(int[] a) { if (a == null) return; if (a.Length > 0) if (a[0] == ) throw new Exception("bug"); } void CoverMe(int[] a) { if (a == null) return; if (a.Length > 0) if (a[0] == ) throw new Exception("bug"); } Observed constraints a==null a!=null && !(a.Length>0) a!=null && a.Length>0 && a[0]!= a!=null && a.Length>0 && a[0]== Data null {} {0} {123…} a==null a.Length>0 a[0]==123… T T F T F F Execute&Monitor Solve Choose next path Done: There is no path left. Negated condition

15 There are decision procedures for individual path conditions, but…  Number of potential paths grows exponentially with number of branches  Reachable code not known initially  Without guidance, same loop might be unfolded forever Fitnex search strategy [Xie et al. DSN 09]

16 public bool TestLoop(int x, int[] y) { if (x == 90) { for (int i = 0; i < y.Length; i++) if (y[i] == 15) x++; if (x == 110) return true; } return false; } TestLoop(0, {0}) Path condition: !(x == 90) ↓ New path condition: (x == 90) ↓ New test input: TestLoop(90, {0})

17 public bool TestLoop(int x, int[] y) { if (x == 90) { for (int i = 0; i < y.Length; i++) if (y[i] == 15) x++; if (x == 110) return true; } return false; } TestLoop(90, {0}) Path condition: (x == 90) && !(y[0] ==15) ↓ New path condition: (x == 90) && (y[0] ==15) ↓ New test input: TestLoop(90, {15})

18 public bool TestLoop(int x, int[] y) { if (x == 90) { for (int i = 0; i < y.Length; i++) if (y[i] == 15) x++; if (x == 110) return true; } return false; } TestLoop(90, {15}) Path condition: (x == 90) && (y[0] ==15) && !(x+1 == 110) ↓ New path condition: (x == 90) && (y[0] ==15) && (x+1 == 110) ↓ New test input: No solution!?

19 public bool TestLoop(int x, int[] y) { if (x == 90) { for (int i = 0; i < y.Length; i++) if (y[i] == 15) x++; if (x == 110) return true; } return false; } TestLoop(90, {15}) Path condition: (x == 90) && (y[0] ==15) && (0 < y.Length) && !(1 < y.Length) && !(x+1 == 110) ↓ New path condition: (x == 90) && (y[0] ==15) && (0 < y.Length) && (1 < y.Length)  Expand array size

20 public bool TestLoop(int x, int[] y) { if (x == 90) { for (int i = 0; i < y.Length; i++) if (y[i] == 15) x++; if (x == 110) return true; } return false; } TestLoop(90, {15}) We can have infinite paths! Manual analysis  need at least 20 loop iterations to cover the target branch Exploring all paths up to 20 loop iterations is infeasible: 2 20 paths

21 public bool TestLoop(int x, int[] y) { if (x == 90) { for (int i = 0; i < y.Length; i++) if (y[i] == 15) x++; if (x == 110) return true; } return false; } Key observations: with respect to the coverage target  not all paths are equally promising for branch-node flipping  not all branch nodes are equally promising to flip Our solution: –Prefer to flip branch nodes on the most promising paths –Prefer to flip the most promising branch nodes on paths –Fitness function to measure “promising” extents TestLoop(90, {15, 0}) TestLoop(90, {15, 15}) [Xie et al. DSN 2009]

22  FF computes fitness value (distance between the current state and the goal state)  Search tries to minimize fitness value [ Tracey et al. 98, Liu at al. 05, …]

23 public bool TestLoop(int x, int[] y) { if (x == 90) { for (int i = 0; i < y.Length; i++) if (y[i] == 15) x++; if (x == 110) return true; } return false; } Fitness function: |110 – x |

24 public bool TestLoop(int x, int[] y) { if (x == 90) { for (int i = 0; i < y.Length; i++) if (y[i] == 15) x++; if (x == 110) return true; } return false; } (90, {0}) 20 (90, {15}) 19 (90, {15, 0}) 19 (90, {15, 15}) 18 (90, {15, 15, 0}) 18 (90, {15, 15, 15}) 17 (90, {15, 15, 15, 0}) 17 (90, {15, 15, 15, 15}) 16 (90, {15, 15, 15, 15, 0}) 16 (90, {15, 15, 15, 15, 15}) 15 … Fitness Value (x, y) Fitness function: |110 – x | Give preference to flip paths with better fitness values We still need to address which branch node to flip on paths …

25 public bool TestLoop(int x, int[] y) { if (x == 90) { for (int i = 0; i < y.Length; i++) if (y[i] == 15) x++; if (x == 110) return true; } return false; } (90, {0}) 20 (90, {15})  flip b4 19 (90, {15, 0})  flip b2 19 (90, {15, 15})  flip b4 18 (90, {15, 15, 0})  flip b2 18 (90, {15, 15, 15})  flip b4 17 (90, {15, 15, 15, 0})  flip b2 17 (90, {15, 15, 15, 15})  flip b4 16 (90, {15, 15, 15, 15, 0})  flip b2 16 (90, {15, 15, 15, 15, 15})  flip b4 15 … Fitness Value (x, y) Fitness function: |110 – x | Branch b1: i < y.Length Branch b2: i >= y.Length Branch b3: y[i] == 15 Branch b4: y[i] != 15 Flipping Branch b4 (b3) gives us average 1 (-1) fitness gain (loss) Flipping branch b2 (b1) gives us average 0 fitness gain (loss)

26  For a flipped node leading to Fnew, find out the old fitness value Fold before flipping Assign Fitness Gain (Fold – Fnew) for the branch of the flipped node Assign Fitness Gain (Fnew – Fold ) for the other branch of the branch of the flipped node  Compute the average fitness gain for each branch over time

27  Each branch node candidate for being flipped is prioritized based on its composite fitness value: (Fitness value of node – Fitness gain of its branch)  Select first the one with the best composite fitness value

28  Pex (released on May 2008):  30,388 download# (20 months, Feb 08-Oct 09)  Active user community: 1,436 forum posts during ~3 years (Oct 08- Nov 11)  Moles (released Sept 2009)  Shipped with VS 12 as Fakes  “Provide Microsoft Fakes w/ all Visual Studio editions” got 1,457 community votes  Code Digger (released on Oct 2008 for VS 08/10, on Apr 2013 in VS Gallery for VS 12)  22,466 download# (10 months, Apr 13-Jan 14) How to make such successful case????

29 void TestAdd(ArrayList a, object o) { Assume.IsTrue(a!=null); int i = a.Count; a.Add(o); Assert.IsTrue(a[i] == o); } Parameterized Unit Tests Supported by Pex Moles/Fakes Code Digger Pex4Fun/Code Hunt  Surrounding (Moles/Fakes)  Simplifying (Code Digger)  Retargeting (Pex4Fun/Code Hunt)

30  Developer/manager: “Who is using your tool?”  Pex team: “Do you want to be the first?”  Developer/manager: “I love your tool but no.” Tool Adoption by (Mass) Target Users Tool Shipping with Visual Studio Macro Perspective Micro Perspective

31  Developer: “Code digger generates a lot of “\0” strings as input. I can’t find a way to create such a string via my own C# code. Could any one show me a C# snippet? I meant zero terminated string.”  Pex team: “In C#, a \0 in a string does not mean zero- termination. It’s just yet another character in the string (a very simple character where all bits are zero), and you can create as Pex shows the value: “\0”.”  Developer: “Your tool generated “\0””  Pex team: “What did you expect?”  Developer: “Marc.”

32  Developer: “Your tool generated a test called Foo001. I don’t like it.”  Pex team: “What did you expect?”  Developer:“Foo_Should_Fail_When_Bar_Is_Negative.”

33 Object Creation messages suppressed (related to Covana by Xiao et al. [ICSE’11]) Exception Tree View Exploration Tree View Exploration Results View

34 public bool TestLoop(int x, int[] y) { if (x == 90) { for (int i = 0; i < y.Length; i++) if (y[i] == 15) x++; if (x == 110) return true; } return false; } Key observations: with respect to the coverage target  not all paths are equally promising for branch-node flipping  not all branch nodes are equally promising to flip Our solution: –Prefer to flip branch nodes on the most promising paths –Prefer to flip the most promising branch nodes on paths –Fitness function to measure “promising” extents Fitnex by Xie et al. [DSN’09] To avoid local optimal or biases, the fitness-guided strategy is integrated with Pex’s fairness search strategies

35  “Simply one mouse click and then everything would work just perfectly”  Often need environment isolation w/ Moles/Fakes or factory methods, …  “One mouse click, a test generation tool would detect all or most kinds of faults in the code under test”  Developer: “Your tool only finds null references.”  Pex team: “Did you write any assertions?”  Developer: “Assertion???”  “I do not need test generation; I already practice unit testing (and/or TDD). Test generation does not fit into the TDD process”

36 Gathered feedback from target tool users  Directly, e.g., via  MSDN Pex forum, tech support, outreach to MS engineers and.NET user groups  Indirectly, e.g., via  interactions with MS Visual Studio team (a tool vendor to its huge user base)  Motivations of Moles  Refactoring testability issue faced resistance in practice  Observation at Agile 2008: high attention on mock objects and tool supports

37  Win-win collaboration model  Win (Ind Lab): longer-term research innovation, man power, research impacts, …  Win (Univ): powerful infrastructure, relevant/important problems in practice, both research and industry impacts, …  Industry-located Collaborations  Faculty visits, e.g., Fitnex, Pex4Fun  Student internships, e.g., FloPSy, DyGen, state cov  Academia-located Collaborations

38 Academia-located Collaborations  Immediate indirect impacts, e.g.,  Reggae [ASE’09s]  Rex  MSeqGen [FSE’09]  DyGen  Guided Cov [ICSM’10]  state coverage  Long-term indirect impacts, e.g.,  DySy by Csallner et al. [ICSE’08]  Seeker [OOPSLA’11]  Covana [ICSE’11]

39  Pex  practice impacts  Moles/Fakes, Code Digger, Pex4Fun/Code Hunt  Lessons in transferring tools  Started as (Evolved) Dream  Chicken and Egg  Human Factors  Best vs. Worst Cases  Tool Users’ Stereotypical Mindset or Habits  Practitioners’ Voice  Collaboration w/ Academia

40 https://sites.google.com/site/asergrp/

41  Pex  practice impacts  Moles/Fakes, Code Digger, Pex4Fun/Code Hunt  Lessons in transferring tools  Started as (Evolved) Dream  Chicken and Egg  Human Factors  Best vs. Worst Cases  Tool Users’ Stereotypical Mindset or Habits  Practitioners’ Voice  Collaboration w/ Academia


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